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Coronavirus Tips PowerPoint Template

Mar 28, 2020 | Data Dashboards , DataPoint , DataPoint Real-time Screens , Healthcare

To help with the current coronavirus emergency, we have put together these free coronavirus tips Powerpoint slide templates for you to use. The slides include coronavirus prevention tips, symptoms and instructions on what to do if you have symptoms.

Feel free to adjust the templates based on your local health authority instructions and contact information. Download the templates by providing your name and email address below.

Coronavirus Tips – Symptoms

This slide shows the common coronavirus symptoms and gives patients instructions on what to do if they have symptoms so that they don’t go to the hospital or doctor in a panic and overload the local health system. You can edit this slide or add additional slides to add local phone numbers, websites or other resource information.

nice template

I would like to download and amend the COVID slides please.

Solved over chat. Now you can use the presentation. Good luck with it!

I would like to download the template as well.

Did you sign up Nicole? That should be working. Check your spam folder maybe.

Excellent template , I will use for professional and public presentations.

Great to read. Thanks.

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COVID-19 Overview and Infection Prevention and Control Priorities in non-U.S. Healthcare Settings

Summary of changes: itf ipc covid-19 overview and infection prevention and control priorities in non-u.s. healthcare settings webpage.

• Updated transmission section to reflect CDC guidance
• Added information on certain activities that can increase risk of COVID-19 infection with references to ventilation guidance by CDC and WHO
• Updated and re-arranged list of symptoms on webpage to reflect current CDC guidance
• Removed section on Illness Severity
• Amended information and references in People at Higher Risk for Severe Illness, noting that these people should be prioritized for vaccination
• Revised COVID-19 Preventative actions section to reflect vaccination, masks, and additional preventative actions that should be continued
• Added additional references to preventative actions section
• Removed treatment section
• Added information on IPC in the context of vaccination delivery
• Revised list of Aerosol Generating Procedures based on updated guidance from WHO and updated references
• COVID-19 Background

Transmission

• People at Higher Risk
• Prevention and Treatment
• What is IPC?

COVID-19 Overview and Infection Prevention and Control Priorities in non-US Healthcare Settings [PPT – 5 MB]

This overview was created for healthcare workers in non-U.S. healthcare settings and government officials at ministries of health working on the Coronavirus Disease 2019 (COVID-19) response.

The information in this document draws from the Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) guidance documents and Infection Prevention and Control (IPC) priorities for the response to COVID-19 in healthcare settings and includes information that can be used in non-U.S. contexts.

Structure of the document

This overview is organized by first presenting a background on coronaviruses. It then briefly describes the emergence, transmission, symptoms, prevention, and treatment of COVID-19. The rest of the document reviews COVID-19 IPC priorities, in non-U.S. healthcare settings.

Coronavirus Background:

Coronaviruses are a large family of viruses that can cause illness in animals or humans. In humans there are several known coronaviruses that cause respiratory infections. These coronaviruses range from the common cold to more severe diseases such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19.

Coronavirus Disease 2019:

COVID-19 was identified in Wuhan, China in December 2019. COVID-19 is caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a new virus in humans causing respiratory illness which can be spread from person-to-person. Early in the outbreak, many patients were reported to have a link to a large seafood and live animal market; however, later cases with no link to the market confirmed person-to-person transmission of the disease. Additionally, travel-related exportation of cases occurred.

There are three main ways that COVID-19 can spread:

• By breathing in air carrying droplets or aerosol particles that contain the SARS-CoV-2 virus when close to an infected person or in poorly ventilated spaces with infected persons
• By having droplets and particles that contain the SARS-CoV-2 virus land on the eyes, nose, or mouth – especially through splashes and sprays like a cough or sneeze
• By touching the eyes, nose, or mouth with hands that have the SARS-CoV-2 virus particles on them

The droplets that contain the SARS-CoV-2 virus are released when someone with COVID-19 sneezes, coughs, or talks. Infectious droplets can land in the mouths or noses of people who are nearby or possibly be inhaled into the lungs. A physical distance of at least 1 meter (3 ft) between persons is recommended by the WHO to avoid infection, 1 whereas CDC recommends maintaining a physical distance of at least 1.8 meters (6ft) between persons. Respiratory droplets can land on hands, objects, or surfaces around the person when they cough or talk, and people can then become infected with COVID-19 from touching hands, objects or surfaces with droplets and then touching their eyes, nose, or mouth. Additionally, transmission can occur from those with mild symptoms or from those who do not feel ill.

There are certain circumstances that can increase the risk of infection for COVID-19 such as poorly ventilated space. In indoor spaces with poor ventilation, the concentration of virus particles is often higher than outdoors. 2,3 Other factors that are associated with increased COVID-19 risk include prolonged exposure to those infected with COVID-19, close contact with infected persons, and any other activity that leads to exposure to a greater amount of respiratory droplets and particles.

A wide range of symptoms for COVID-19 have been reported. 4 These symptoms include:

• Fever or chills
• Muscle or body aches
• Sore throat
• Nasal congestion or runny nose
• Shortness of breath or difficulty breathing
• Loss of smell or taste

The estimated incubation period is between 2 and 14 days with a median of 5 days. It is important to note that some people become infected and do not develop any symptoms or feel ill.

People at Higher Risk for Severe Illness

COVID-19 is a relatively new disease; therefore, additional risk factors for severe COVID-19 may continue to be identified. In some cases, people who get COVID-19 can develop severe complications, including difficulty breathing, causing a need for hospitalization and intensive care. 5 These severe complications often lead to death. The risk of severe disease increases steadily as people age. Additionally, those of all ages with underlying medical conditions , including but not limited to heart disease, diabetes or lung disease, are  at higher risk to develop severe COVID-19 compared to those without these conditions. 5 Those at higher risk for severe illness should be prioritized for vaccination. 6

COVID-19 Preventative Actions

COVID-19 can be prevented through pharmaceutical (i.e., vaccination) and non-pharmaceutical interventions (e.g., masking, physical distancing, hand hygiene). All of these preventative measures are important to protect individuals from acquiring and transmitting the SARS-CoV-2 virus and should be done in conjunction with one another.

Vaccination

Getting vaccinated is a preventative measure that people can take to avoid getting sick with COVID-19 and to avoid infecting others. While safe and effective vaccines are a great tool for prevention, it is important to continue other preventative actions such as wearing masks, performing hand hygiene, physically distancing from others, and avoiding crowded spaces and spaces with poor ventilation. 7  There are several vaccine candidates, and many have been listed under WHO’s emergency use.

Wearing masks is another important preventative action for COVID-19 that should continue to be performed. When selecting a mask, there are many factors to consider. 8 Masks should

• Have two or more layers of washable, breathable fabric
• Completely cover the nose and mouth
• Fit snug against the sides of the face and not have gaps
• Have a nose wire to prevent air from leaking out of the top of the mask

It is also important to choose and wear the proper type of mask based on your setting. For example, in a community setting, cloth masks should be worn, whereas during aerosol generating procedures in a healthcare setting, should be worn.

Other preventative actions

Even with the introduction of vaccinations as a tool for prevention against COVID-19 and the proper use of masks, CDC recommends the following key COVID-19 preventative activities: 7 — avoiding crowded spaces or spaces that have poor ventilation or wear a mask in these spaces; performing proper hand hygiene; keeping high touch surfaces clean; monitoring symptoms; and getting tested if ill.

IPC for COVID-19

Infection prevention and control (IPC) is the practice of preventing or stopping the spread of infections from the delivery of healthcare services in facilities like hospitals, outpatient clinics, dialysis centers, long-term care facilities, or traditional practitioners. IPC is a critical part of health system strengthening and must be a priority to protect patients and healthcare workers. In the context of COVID-19, the IPC goal is to support the maintenance of essential healthcare services by containing and preventing COVID-19 transmission within healthcare facilities to keep patients and healthcare workers healthy and safe.

COVID-19 IPC Priorities

• Rapid identification of suspect cases
• Screening/triage at initial healthcare facility encounter and rapid implementation of source control
• Limiting the entry of healthcare workers and/or visitors with suspected or confirmed COVID-19
• Immediate isolation and referral for testing
• Group patients with suspected or confirmed COVID-19 separately
• Test all suspected patients for COVID-19
• Safe clinical management
• Immediate identification of inpatients and healthcare workers with suspected COVID-19
• Adherence to IPC practices
• Appropriate use of personal protective equipment (PPE)
• Unvaccinated healthcare workers, patients, and visitors should be offered resources and counseled about the importance of vaccination.

COVID-19 IPC in the context of vaccination delivery

As safe and effective COVID-19 vaccines continue to be delivered, there are certain recommendations and principles that should be implemented and considered for vaccine administration. Consultations and consensus between WHO, the United Nations Children’s Fund (UNICEF), and the ad hoc WHO COVID-19 IPC Guidance Development Group have led to the development of a document that outlines key IPC principles and recommended proper precautions for safe administration of COVID-19 vaccines. Consult the Infection prevention and control (IPC) principles and procedures for COVID-19 vaccination activities  document when preparing for vaccine deployment. 10

The key IPC principles for COVID-19 vaccine deployment set out in the document include:

• Applying standard precautions during any vaccination activity
• Performing additional IPC precautions like mask use in the context of the COVID-19 pandemic
• Providing healthcare workers with specific training and providing the public with targeted information regarding IPC measures for safe vaccine delivery
• Having a clean, hygienic, and well-ventilated environment with appropriate waste management, and adequate spaces that facilitate best IPC practices like physical distancing
• Ensuring consultation and adherence to national guidance and protocols for IPC measures, including those related to COVID-19

Standard and Transmission-Based Precautions

Standard precautions are a set of practices that apply to the care of patients in all healthcare settings at all times. Standard precautions remain the cornerstone of infection prevention and control. Application of these precautions depends on the nature of the healthcare worker-patient interaction and the anticipated exposure to a known infectious agent. Standard precautions include:

• Hand hygiene
• Personal protective equipment
• Respiratory hygiene and cough etiquette
• Cleaning and disinfection of devices and environmental surfaces
• Safe injection practices
• Medication storage and handling

Transmission-based precautions are a set of practices specific for patients with known or suspected infectious agents that require additional control measures to prevent transmission. These precautions are used in addition to standard precautions.

COVID-19 Transmission-Based Precautions:

Current WHO guidance for healthcare workers caring for suspected or confirmed COVID-19 patients recommends the use of contact and droplet precautions in addition to standard precautions unless an aerosol generated procedure is being performed, in which case airborne precautions are needed. 1 Disposable or dedicated patient care equipment, such as stethoscopes, blood pressure cuffs, should be used. If equipment needs to be shared among patients, it should be cleaned and disinfected between use for each patient using products containing ethyl alcohol of at least 70%.

Also, adequately ventilated single rooms or wards are suggested. For general ward rooms with natural ventilation, adequate ventilation for COVID-19 patients is considered to be 60 L/s per patient. When single rooms are not available, suspected COVID-19 patients should be grouped together with beds at least 1 meter (3ft) apart based on WHO’s recommendations, although some member states, including the United States, have recommended maintaining greater distances whenever possible. COVID-19 isolation rooms or wards should have dedicated bathrooms, which should be cleaned and disinfected at least twice daily.

Additionally, healthcare facilities can also consider designating healthcare workers to care for patients with COVID-19 and restricting the number of visitors allowed in the facility.

Transportation of patients with COVID-19 should be avoided unless medically necessary. If transportation is deemed medically necessary, a mask should be placed on the suspected or confirmed COVID-19 patient. Healthcare workers should also wear the appropriate PPE when transporting patients.

COVID-19 PPE

Contact and droplet precaution PPE are recommended for healthcare workers before entering the room of suspected or confirmed COVID-19 patients. Healthcare workers should be trained on the correct use of PPE, including how to put it on and remove it. Extended use and re-use of certain PPE items such as masks and gowns can be considered when there are supply shortages. Healthcare workers should:

• Use a medical mask (at least a surgical/medical mask)
• Wear eye protection (goggles) or facial protection (face shield)
• Wear a clean, non-sterile, long-sleeve gown

There is a higher risk of self-contamination when removing PPE. Please see instructions for putting on and removing PPE [2.9 MB, 3 pages]  for guidance.

For healthcare workers performing any of the following aerosol generating procedures on patients with COVID-19, it is recommended that a fitted respirator mask (surgical N95 respirators, FFP2 or equivalent) is used as opposed to surgical/medical masks. In addition to wearing a fitted respirator mask, healthcare workers should also wear appropriate PPE, including gloves, a gown and eye protection.

Although there is a difference in determination on which procedures generate infectious aerosol, the current WHO list of Aerosol Generating Procedures includes: 1

• Endotracheal intubation
• Bronchoscopy
• Non-invasive ventilation
• Tracheotomy
• Manual ventilation before intubation
• Cardiopulmonary resuscitation
• Sputum induction
• Dentistry and autopsy procedures

Infection Prevention and Control Resources for COVID-19 in non-U.S. Healthcare Settings:

• Strategic Priority IPC Activities for Containment and Prevention
• Identification of Healthcare Workers and Inpatients with Suspected COVID-19
• WHO. Infection prevention and control during health care when novel coronavirus disease (COVID-19) is suspected or confirmed .29 June 2020.
• Kai-Wang To, K., Tak-Yin Tsang, O., Chik-Yan Yip, C., Chan, KH., Wu, TC., Man-Chun Chan, J…Yuen, KY. Consistent detection of 2019 novel coronavirus in saliva . Clinical Infectious Diseases . 12 February 2020. ciaa149.
• WHO. Transmission of SARS-CoV-2: implications for infection prevention precautions . 9 July 2020.
• WHO. Clinical management of severe acute respiratory infection when COVID-19 is suspected . 13 March 2020.
• The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) – China 2020 . CDCweekly . 17 February 2020. 10.46234/ccdcw2020.032
• Stokes EK, Zambrano LD, Anderson KN, et al. Coronavirus Disease 2019 Case Surveillance — United States, January 22–May 30, 2020. MMWR Morb Mortal Wkly Rep 2020;69:759–765. DOI: http://dx.doi.org/10.15585/mmwr.mm6924e2
• Chen, J., Lu, H., Melino, G. et al. COVID-19 infection: the China and Italy perspectives. Cell Death Dis 11, 438 (2020). https://doi.org/10.1038/s41419-020-2603-0
• WHO. Advise on the use of masks in the context of COVID-19 . 5 June 2020.

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Coronavirus (COVID-19) Impact Implications & Immediate Actions PowerPoint Template

Product Description:

Coronavirus (covid-19) impact implications & immediate actions presentation template.

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236 Best Coronavirus-Themed Templates for PowerPoint & Google Slides

With over 6 million presentation templates available for you to choose from, crystalgraphics is the award-winning provider of the world’s largest collection of templates for powerpoint and google slides. so, take your time and look around. you’ll like what you see whether you want 1 great template or an ongoing subscription, we've got affordable purchasing options and 24/7 download access to fit your needs. thanks to our unbeatable combination of quality, selection and unique customization options, crystalgraphics is the company you can count on for your presentation enhancement needs. just ask any of our thousands of satisfied customers from virtually every leading company around the world. they love our products. we think you will, too" id="category_description">crystalgraphics creates templates designed to make even average presentations look incredible. below you’ll see thumbnail sized previews of the title slides of a few of our 236 best coronavirus templates for powerpoint and google slides. the text you’ll see in in those slides is just example text. the coronavirus-related image or video you’ll see in the background of each title slide is designed to help you set the stage for your coronavirus-related topics and it is included with that template. in addition to the title slides, each of our templates comes with 17 additional slide layouts that you can use to create an unlimited number of presentation slides with your own added text and images. and every template is available in both widescreen and standard formats. with over 6 million presentation templates available for you to choose from, crystalgraphics is the award-winning provider of the world’s largest collection of templates for powerpoint and google slides. so, take your time and look around. you’ll like what you see whether you want 1 great template or an ongoing subscription, we've got affordable purchasing options and 24/7 download access to fit your needs. thanks to our unbeatable combination of quality, selection and unique customization options, crystalgraphics is the company you can count on for your presentation enhancement needs. just ask any of our thousands of satisfied customers from virtually every leading company around the world. they love our products. we think you will, too.

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• Published: 21 May 2024

Effectively teaching cultural competence in a pre-professional healthcare curriculum

• Karen R. Bottenfield 1 ,
• Maura A. Kelley 2 ,
• Shelby Ferebee 3 ,
• Andrew N. Best 1 ,
• David Flynn 2 &
• Theresa A. Davies 1 , 2  

BMC Medical Education volume  24 , Article number:  553 ( 2024 ) Cite this article

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There has been research documenting the rising numbers of racial and ethnic minority groups in the United States. With this rise, there is increasing concern over the health disparities that often affect these populations. Attention has turned to how clinicians can improve health outcomes and how the need exists to educate healthcare professionals on the practice of cultural competence. Here we present one successful approach for teaching cultural competence in the healthcare curriculum with the development of an educational session on cultural competence consisting of case-based, role-play exercises, class group discussions, online discussion boards, and a lecture PowerPoint presentation.

Cultural competence sessions were delivered in a pre-dental master’s program to 178 students between 2017 and 2020. From 2017 to 2019, the sessions were implemented as in-person, case-based, role-play exercises. In 2020, due to in-person limitations caused by the COVID-19 pandemic, students were asked to read the role-play cases and provide a reflection response using the online Blackboard Learn discussion board platform. Evaluation of each session was performed using post-session survey data.

Self-reported results from 2017 to 2020 revealed that the role-play exercises improved participant’s understanding of components of cultural competence such as communication in patient encounters (95%), building rapport with patients (94%), improving patient interview skills (95%), and recognition of students own cultural biases when working with patients (93%).

Conclusions

Students were able to expand their cultural awareness and humility after completion of both iterations of the course session from 2017 to 2019 and 2020. This session can be an effective method for training healthcare professionals on cultural competence.

Peer Review reports

It is projected that by the year 2050, racial and ethnic minority groups will make up over 50% of the United States population [ 1 ]. With a more multicultural society, growing concern has emerged over how to address the health disparities that effect these populations and the ways in which healthcare professionals can increase positive health outcomes. Continuing evidence suggests that many patients from racial and ethnic minority groups are not satisfied with the current state of healthcare which has been attributed to implicit bias on the part of physicians and current challenges faced by practitioners who feel underprepared to address these issues due to differences in language, financial status, and healthcare practice [ 2 , 3 , 4 ].

To contend with health disparities and the challenges faced by practitioners working with a more diverse population, healthcare educators have begun to emphasize the importance of educating healthcare workforce on the practice of cultural competence and developing a skilled-based set of behaviors, attitudes and policies that effectively provides care in the wake of cross-cultural situations and differences [ 4 , 5 , 6 ]. There are several curricular mandates from both medical and dental accreditation bodies to address this issue [ 7 , 8 , 9 ], and large amounts of resources, ideas, and frameworks that exist for implementing and training future and current healthcare providers on the inadequacies of the healthcare system and cultural competence [ 10 , 11 , 12 ]. These current institutional guidelines for accreditation and the numerous amounts of resources for training cultural competence, continue to evolve with work documenting the need for blended curriculum that is continuous throughout student education, starting early as we have done here with pre-dental students, including in-person didactic or online sessions, a service learning component, community engagement and a reflective component [ 4 , 5 , 13 , 14 ].

This study investigates teaching cultural competence in a healthcare curriculum. We hypothesized that early educational exposure to cultural competence through role playing case studies, can serve as an effective mechanism for training early pre-doctoral students the practice of cultural competence. Utilizing student self-reported survey data conducted in a predental master’s curriculum, in which two iterations of role-playing case studies were used to teach components of cultural competence, this study aims to evaluate and support research that suggests role-playing case studies as effective means for educating future clinical professionals on the practice of cultural competence.

This study was determined to be exempt by the Institutional Review Board of Boston University Medical Campus, Protocol # H-37,232. Informed consent was received from all subjects.

Data collection

The role-playing, case-based simulated patient encounter exercises were developed and administered at Boston University Chobanian & Avedisian School of Medicine to predental students in the Master of Science in Oral Health Sciences Program (see Table  1 ). From 2017 to 2020, we administered patient encounter cases [see Additional File 1 ] to students ( n  = 178) in the program as a portion of a case-based, role-playing exercise to teach the importance of cultural competence and cultural awareness during patient encounters. During years 2017–2019, real actors portrayed the patient and physician. In 2020, the session was conducted online via a discussion board through a Blackboard Course Site. The original case was published as part of a master’s students thesis work in 2021 [ 15 ].

Description of patient encounter cases 1 and 2

Patient Encounter Case 1 [see Additional file 1 ] is composed of two subsections, scenario 1 A and scenario 1B, and is centered around a patient/physician interaction in which a patient who is pregnant presents with pain upon urination. The physician in 1 A is short and terse with the patient, immediately looking at a urine sample, prescribing medication for a urinary tract infection, and telling the patient to return for a follow-up in 2 weeks. In scenario 1B, a similar situation ensues; however, in this scenario the physician takes more time with the patient providing similar care as the physician in 1 A, but asking for more information about the patients personal and medical history. At the conclusion of the scenario, the patient is offered resources for an obstetrician and a dentist based on the information that is provided about the patient’s background. The patient is then sent on their way and asked to follow-up in 2 weeks. The patient does not return.

Patient Encounter Case 2 [see Additional file 1 ] follows a similar format to the Patient Encounter Case 1. In scenario 2 A, the same patient from Case 1 returns with tooth pain after giving birth. The physician in 2 A, like 1 A, is short with the patient and quickly refers the patient to a dentist. In 2B, the physician again takes more time with the patient to receive background information on the patient, make a connection, and provides an antibiotic and dental referral.

Each Patient Encounter Case explored topics such as the importance of building a trusting physician/patient relationship, the importance of asking a patient for patient history, making a connection, and the importance of a physician taking all facets of a patient’s circumstances into consideration [ 15 ].

Session outline

The sessions conducted between 2017 and 2019 were composed of three parts: (1) enactment of an abridged patient encounter facilitated by session administrators, (2) group discussion and reflection during which time students were asked to critically reflect and discuss the theme and key take-aways from the role play exercise, and (3) a PowerPoint presentation emphasizing take-away points from the role-play exercise. At the conclusion of the cultural competence training sessions, students participated in a post-session Qualtrics generated survey administered electronically to assess each student’s feelings about the session [see Additional file 3 ].

Role-play enactment

Facilitators dressed-up in clothing to mimic both the physician and patient for all case scenarios in Patient Encounter Case 1 and Case 2. At the conclusion of the role play portion of each of the cases, the facilitators paused to lead students in a real-time class group discussion. After Case 1, students were asked questions such as: What did you think ? Were the patient’s needs met? Did you expect the patient to return? Following Case 2, similar questions were asked by the facilitators, including: What did you think ? Were the patient’s needs met? Did you expect the patient to accept help?

At the conclusion of this portion of the session, the facilitators led a larger general discussion about both cases and how they related to one another. Finally, the course session concluded with a PowerPoint presentation that reinforced the take-home points from the session [see Additional file 2 ] [ 15 ].

Change in session modality due to COVID-19 pandemic

In Fall 2020, due to the COVID-19 pandemic, the course modality moved to an online platform and consisted of three parts on a Blackboard Discussion Board (Blackboard, Inc.). Students were required to: (1) read each of the Patient Encounter Cases and add a brief reflection comparing the scenarios, (2) then comment on at least two peer’s posts in the discussion forum and (3) attend class to hear a PowerPoint presentation by a course session facilitator on the key take-aways from each scenario [ 15 ].

Student surveys

At the conclusion of the cultural competence training sessions, students participated in a post-session Qualtrics ( https://www.qualtrics.com ) generated survey administered electronically to assess each student’s feelings about the sessions [see Additional file 3 ]. The format of the survey included 5 questions with the following Likert scale response options: strongly agree, agree, disagree, strongly disagree. These post-session surveys were not required but rather optional [ 15 ].

A total of 178 students completed the cultural competence sessions between 2017 and 2020. Of these participants, 112 voluntarily completed a post-session survey on the effectiveness of the course in teaching cultural competence and cultural awareness during patient encounters. Between 2017 and 2019, 99 students completed post-session surveys following sessions with role play exercises. In 2020, 13 students completed post-session surveys following discussion board sessions.

Role-play exercises enhanced cultural competence

In responding to post-session survey questions following cultural competence sessions that included role-play exercises (2017–2019), 71% of students surveyed strongly agreed and 24% agreed that the role-play exercises helped them to identify the importance of communication in patient encounters. In asking participants if the role-play exercises made them more aware of different strategies to improve their patient interview skills, 72% strongly agreed and 23% agreed. Also, 68% of the students strongly agreed and 26% agreed that the exercises helped them to better identify the importance of building rapport and trust during patient encounters. When asked if the exercises helped the students to better understand their own bias and/or cultural awareness when working with patients, the results of the survey showed that 62% of students strongly agreed and 31% agreed with this statement. In addition, most students found the role-play exercises to be enjoyable (72% strongly agreed and 22% agreed). See results shown in Fig.  1 .

Cultural Competence Session Survey Data from the Year 2017–2019. Survey data from students at Boston University’s Oral Health Sciences Program for the years 2017–2019. Data is presented as percent of respondents ( n  = 99)

Discussion boards and reflections enhanced cultural competence

Cultural competence sessions held during 2020 did not include role-play exercises due to the Covid-19 pandemic. Instead, students participated in discussion boards and reflections on Blackboard. In response to the post-session survey question asking if the discussion board exercises were helpful in identifying the importance of communication during patient encounters, 67% of students strongly agreed and 25% agreed with this statement. Also, 75% of students strongly agreed and 17% agreed that the discussion board exercises helped them identify the importance of building rapport and trust during patient contact. When asked if the exercises helped the students to better understand their own bias and/or cultural awareness when working with patients, the results of the survey showed that 67% of students strongly agreed and 25% agreed with this statement. In addition, most students found the discussion board exercises to be enjoyable (67% strongly agreed and 22% agreed). See results shown in Fig.  2 .

Cultural competence session survey data from the Year 2020. Survey data from students at Boston University’s Oral Health Sciences Program for the year 2020. Data is presented as percent of respondents ( n  = 13)

Student responses to the reflection portion of the online cultural competency sessions were recorded and categorized. Five themes were selected and 441 reflection responses were coded using NVivo (Version 12). The results showed that 29% of reflections demonstrated student’s ability to understand a holistic approach to clinical care, 24.3% understood the importance of collecting a patient history, 6.8% recognized the socioeconomic factors during a patient encounter, 27.9% reflected on the importance of the patient clinical relationship, and 12% on the effects on improving health outcomes (Table  1 ). Representative student responses to these themes are shown in Table  1 .

There exists a need to develop novel and effective means for teaching and training the next generation of healthcare professionals the practice of cultural competence. Thus, two iterations of a course session using case-based patient centered encounters were developed to teach these skills to pre-professional dentals students. Overall, the results of this study demonstrated that participation in the course, subsequent group discussion sessions, and take-away PowerPoint sessions significantly improved the participant’s understanding of the importance of communication skills and understanding of socioeconomic, environmental, and cultural disparities that can affect a patient’s health outcome.

According to results from the course session implemented in-person from 2017 to 2019, the role-playing exercise significantly improved participants understanding of important components that can be used to improve health outcomes that may be affected due to health disparities. Students were strongly able to identify the importance of communication in patient encounters, to understand strategies such as communication and compassionate care in patient encounters, identify the importance of building a patient-physician relationship with patients, and were able to recognize their own cultural biases. Similarly, in 2020, even with a change in course modality to on-line learning due to COVID-19, students were able to understand the same key take-aways from the course session as demonstrated by reflections using the discussion board regarding the need for a holistic approach to care, importance of the patient clinician relationship, and importance of taking a patient history. Despite promising implications of both iterations of the session, students completing the session online did not find the same success in “understanding my own bias/and or cultural awareness when working with patients.” This decrease may be attributed to change in course modality and the strengths of the role-play enactment of the patient encounter. It is important to recognize that additional learning components, including video recordings of the role-play enactment, may be necessary if the discussion board is used as the primary learning method in the future.

In contrast to previous studies that attempted to determine the effectiveness of cultural competence training methods, this session had many unique characteristics. The simulated role-playing exercise enabled student participants to see first-hand an interactive patient scenario that could be used as an example for when students begin working with patients or communicating with patients who are culturally diverse. Additionally, the nature of the cases created for the course session which were divided into a part A in which the patient physician was more straightforward when diagnosing and treating the patient and a part B with a more comprehensive and nurturing approach to care, allowed the students to compare the scenarios and make their own assumptions and comments on the effectiveness of each portion of the case. Another strength of this training, was the faculty with cultural competence training were uniquely involved in case creation and facilitation of the course session. According to previous studies with similar aims, it was noted that direct observation and feedback from a faculty member who had cultural competence training and direct contact with patients can provide students with a more memorable and useful experience when educating students [ 12 ]. The facilitators of this session were able to emphasize from their own personal experiences how to work with culturally diverse populations.

An important aspect of the 2020 iteration of the course session in which a discussion board format was used, was that it allowed students who may feel uncomfortable with sharing their thoughts on a case and their own biases, the opportunity to share in a space that may feel safer than in person [ 4 ]. Previous studies have mentioned challenges with online discussion boards [ 4 ] but here we had robust participation, albeit required. Students often contributed more than the required number of comments and they were often lengthy and engaging when responding to peers. Finally, in contrast to previous studies, this course session took place in a pre-professional master’s program, the M.S. in Oral Health Sciences Program at Boston University Chobanian & Avedisian School of Medicine. This program, in which students are given the opportunity to enhance their credentials for professional school, provided students with early exposure to cultural competence training. Students that completed this session in their early pre-professional curriculum should be better prepared than peers who did not receive any cultural competence training until they entered their designated professional school. This session is part of an Evidence Based Dentistry course, which incorporates a larger component of personal reflection that serves to engage students in critical thinking as they begin to develop the skills to be future clinicians. Students that understand different cultures, society and themselves through self-assessments will grow and be best suited in time to treat future patients [ 4 , 16 , 17 ].

One limitation of the present study was the number of survey participants that competed the post-session surveys, as survey completion was not required. Thus, the number of student participants declined over the years, reaching its lowest number of participants in 2020 when the discussion board course session was implemented, and students may have been over surveyed due to the pandemic. Another limitation to this study, was the lack of both a pre and post survey that could be used to determine how student’s understanding of cultural competence had evolved from their entry into the course to the conclusion of the course as well as individual bias and self-reporting measures.

In the future, the course should implement both a role-playing format and subsequent discussion board reflections within the same course session. Studies have shown that alternatives ways of drawing students to reflect whether role play, personal narratives, etc. can be extremely advantageous in developing personal reflection and awareness building competency [ 4 , 16 , 17 , 18 ]. It is noted that role-playing exercises that allow students to provide feedback with student colleagues can provide students with more insight into their own behaviors. It has also been shown in previous studies that student writing and reflection activities can also facilitate student’s reflections on their own beliefs and biases [ 4 , 11 ]. Reflective writing skills are an important and effective means for students to continue to gauge their cultural competence throughout the remainder of their academic training and as future clinicians [ 4 , 17 , 19 ]. Further, students may experience emotional responses through the process of reflective writing as they recognize personal bias or stereotypes, creating a profound and impactful response resulting in enhanced understanding of cultural differences and beliefs [ 4 ]. By combining both learning techniques, students would be able to understand their own bias and their classmates and create a dialogue that could be more beneficial than just one learning method alone. Furthermore, by implementing the discussion board into the role-playing session, as stated previously, students that are more cautious about sharing their point of view or about their own implicit bias in a traditional classroom setting would be able to express their opinions and facilitate a more comprehensive discussion more thoroughly.

Here we show an effective means to utilize role-play of a multi-scenario case-based patient encounter to teach pre-professional healthcare student’s components of cultural competence, emphasizing the importance of provider-patient interactions, holistic patient care, and patient history and socioeconomic factors in provider care. This study contributes to the larger body of work that seeks to address this important aspect of education as it relates to enhancing patient health care outcomes.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to acknowledge Boston University’s Chobanian & Avedisian School of Medicine’s Graduate Medical Science students and study participants.

No funding was used for the completion of this study.

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Karen R. Bottenfield, Andrew N. Best & Theresa A. Davies

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Maura A. Kelley, David Flynn & Theresa A. Davies

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TAD designed the original study concept, taught the classes (roleplay), conducted the surveys, and collected data; MAK designed the original case and PowerPoint, and performed roleplay; DBF and SF evaluated data and drafted original figures; ANB assisted in drafting the manuscript; KRB finalized figures and the manuscript.

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Correspondence to Theresa A. Davies .

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• Published: 21 May 2024

COVID‑19 detection from chest X-ray images using transfer learning

• Enas M. F. El Houby 1  

Scientific Reports volume  14 , Article number:  11639 ( 2024 ) Cite this article

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COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 is based on swabs from the throat and nose, their sensitivity is not high enough and so they are prone to errors. Early diagnosis of COVID-19 disease is important to provide the chance of quick isolation of the suspected cases and to decrease the opportunity of infection in healthy people. In this research, a framework for chest X-ray image classification tasks based on deep learning is proposed to help in early diagnosis of COVID-19. The proposed framework contains two phases which are the pre-processing phase and classification phase which uses pre-trained convolution neural network models based on transfer learning. In the pre-processing phase, different image enhancements have been applied to full and segmented X-ray images to improve the classification performance of the CNN models. Two CNN pre-trained models have been used for classification which are VGG19 and EfficientNetB0. From experimental results, the best model achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95 using enhanced full X-ray images for binary classification of chest X-ray images into COVID-19 or normal with VGG19. The proposed framework is promising and achieved a classification accuracy of 0.935 for 4-class classification.

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Introduction

Since December 2019, coronavirus has been disseminated from China to many other countries. Coronavirus which is called SARS-CoV-2 causes COVID-19 as named by World Health Organization (WHO) on February 11, 2020. World Health Organization announced COVID-19 disease resulted from the coronavirus as a world pandemic in March 2020 1 . The disease has disseminated to nearly all countries, resulting in millions of people’s deaths among confirmed cases based on the statistics of the WHO 2 . By July 2023, nearly 700 million confirmed cases, and almost 7 million confirmed deaths were recorded in the world 3 , 4 . Most patients with the virus experience mild to moderate respiratory illness and heal without needing special treatment. But, some suffer from complications and need medical attention. Older people and those with underlying medical conditions like cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illnesses. Anyone can get sick with COVID-19 and become seriously ill or die at any age 5 .

Although the last diagnosis of COVID-19 depends on transcription-polymerase chain reaction (PCR) tests, in states of people with intensive respiratory symptoms the diagnosis protocol depends on medical imaging, which helps doctors to recognize the disease as the sensitivity of PCR is strongly variable 6 . As chest radiography imaging such as computed tomography (CT) imaging and X-ray have been used successfully for the diagnosis of pneumonia, they have a high sensitivity for the diagnosis of COVID-19 2 . The suspected case undergoes an X-Ray session and if more details are required, a computed tomography scan (CT-scan) session is taken. Therefore, X-ray 7 and CT scan images 8 are being used as diagnostic methods for COVID-19 and to detect the effects 9 of the virus 6 , 10 . The availability and accessibility of X-ray imaging in many imaging centers and clinics is more present even in rural regions as it is standard equipment in healthcare systems. Particularly, chest X-ray is more readily available than CT, because CT scanners require high equipment and maintenance costs. CT is not very suitable for COVID-19 screening as well because of its cost, imaging time, and radiation exposure whereas X-ray is more cost and time effective in dealing with such a common virus 11 .

The abnormalities can only be explained by expert radiologists. With the huge number of suspected cases and the limited number of available radiologists, automatic methodologies for the recognition of these precise abnormalities can aid in early diagnosis with high accuracy. The studies in Artificial Intelligence (AI) and machine learning, especially Deep Learning (DL), achieved high performance in the diagnosis of medical images. Therefore, DL techniques are robust tools for such issues.

Deep learning (DL) has been successfully used to predict COVID-19 from Chest images. Unlike the traditional machine learning techniques DL can be used to predict disease from raw images without feature extraction required. The role of deep learning is to learn the features using a trained model with a huge amount of data to improve the classification's accuracy which reduces the burden on physicians and decreases the effect of doctors’ shortages of the struggle against the disease. Convolutional neural network (CNN) is the type of DL model intended for image analysis tasks and has already been utilized in many medical problems such as segmentation and classification 12 .

Many high-performing pre-trained CNN structures have been provided in the literature to be utilized in similar problems. These models were trained using ImageNet data which contains 1,000,000 images and 1000 classes to overcome the limitation of data and to reduce the training time 13 . These models can be used for image recognition based on transfer learning after fine-tuning these networks to the new problems. The learned weights of these pre-trained models are provided and used directly in the new problems 14 . The purpose of utilizing pre-trained models is to take advantage of learned features on a larger dataset, therefore the new model can converge faster and perform better with a smaller dataset. This gives us the advantage of DL independence of feature engineering over traditional methods without giving up the time, computational resources and cost effeciencies. Examples of these pre-trained CNN models are visual geometry group VGG (16, 19) 15 , EfficientNet (B0 to B7) 16 , MobileNet 17 , and residual neural network (ResNet) 18 , etc.

The contributions of this research can be summarized as follows:

A framework has been developed to diagnose COVID-19 using chest X-ray images for both full and segmented images.

A multiplication between each original image and the associated lung mask from the ground truth dataset provided by the database has been applied to get the segmented lung.

Different image enhancement techniques have been applied to both full and segmented X-ray images to reach the best possible classification performance.

CNN pre-trained models based on transfer learning have been used to classify both full and segmented chest X-ray images with all enhancement versions and achieved promising results.

Since the purpose of utilizing pre-trained models is to take advantage of learned features on a larger dataset, therefore the smallest possible datasets that can achieve the best possible performance have been used for faster convergence.

Recently, many works have been developed to detect and diagnose COVID-19 and other lung diseases based on different medical image modalities using different machine learning techniques especially deep learning and transfer learning techniques. The purpose of all these works is to improve the performances of the methodologies used in the detection and classification of COVID-19 and other lung diseases. The focus in the research will be in X-ray images as the adopted medical image modality in this research.

The rest of the paper is organized as follows. Related COVID-19 articles using deep learning are reviewed in the “ Related work ” section. Then, the proposed framework for COVID-19 classification is described in " The proposed framework " section. Next, the results of X-ray images obtained with the proposed framework are presented in " Experimental results " section. The discussion and comparison with literature are provided in " Discussion " section. Finally, the main “ Conclusions and future work ” are outlined.

Related work

Recently, many works have been developed to detect and diagnose COVID-19 and other lung diseases based on different medical image modalities using different machine learning techniques especially deep learning and transfer learning. The purpose of all these works is to improve the performances of the methodologies used in the detection and classification of COVID-19 and other lung diseases. Where the proposed research will use X-ray images as a medical image modality, the focus in this section will be on the previous work based on X-rays.

Nishio, et al. 19 presented a system based on VGG16 to classify images of chest X-rays as healthy, COVID-19 pneumonia, and non-COVID-19 pneumonia. They applied the proposed system to 1248 X-ray images collected from 2 different public datasets. The collected X-ray images contain 500 healthy samples, 215 images for COVID-19 pneumonia patients and 533 images for non-COVID-19 pneumonia patients. The achieved accuracy was 83.6%, while the sensitivity was 90.9%.

Minaee et al. 20 applied deep learning to recognize COVID-19 cases using chest X-rays images. Transfer learning was used to train 4 CNN models which are DenseNet-121, SqueezeNet, ResNet50, and ResNet18 to binary classify images as COVID-19 or not. The training was applied to 84 (420 after augmentation) COVID-19 images and 2000 non-Covid images, while the test was applied to 100 COVID-19 images and 3000 non-COVID images. The best achieved sensitivity of these models was 98%, while the specificity was 92.9% for the SqueezeNet model.

Sahin 21 proposed a CNN model for binary classification of COVID-19 cases as COVID and Normal using chest X-ray images. Also, two pre-trained models which are ResNet50 and MobileNetv2 are applied to the used dataset of 13,824 X-ray images. The proposed CNN model achieved an accuracy of 96.71% and F1-score of 97%. MobileNetv2 achieved an accuracy of 95.73% and F1-score of 96%, while ResNet50 achieved an accuracy of 91.54% and F1-score of 91%.

Wang et al. 22 developed an open-source CNN called COVID-Net to detect COVID-19 cases using chest X-ray images. The proposed net can predict the case as one of three classes which are COVID-19 viral infection, non-COVID-19 infection, and normal. Also, an open access benchmark dataset COVIDx was introduced, it contains 13,975 X-ray images collected from 13,870 patients. The COVIDx dataset was generated using five different publicaly available datasets. The accuracy of COVID-Net reached 93.3%.

Panwar et al. 23 developed a deep learning model called nCOVnet for detecting COVID-19 based on X-rays. A dataset of 284 X-ray images was used of which 142 images are normal cases and 142 images are COVID-19 cases. The model achieved an accuracy of 88.1%.

Nigam et al. 24 used transfer learning to utilize 5 pre-trained models which are DenseNet121, NASNet, Xception, VGG16, and EfficientNet to classify Coronavirus suspected cases as normal, COVID-19 positive cases, and other classes. The used dataset contains 16,634 X-ray images, 6000 normal images, 5634 COVID images, and 5000 imges for others. The achieved accuracies were 79.01%, 85.03%, 88.03%, 89.96%, and 93.48% for VGG16, NASNet, Xception, DenseNet121, and EfficientNet respectively.

Chow et al. 25 used transfer learning to utilize 18 CNN models including VGG-19, VGG-16, ShufeNet, SqueezeNet. etc. to classify the cases as normal or COVID-19. The used dataset contains 700 X-ray images (350 normal cases and 350 COVID-19 cases) from both public and private institutes. The highest 4 models are VGG-19, VGG-16, ResNet-101, and SqueezeNet with accuracy ranging from 90.7 to 94.3% and F1-score from 90.8 to 94.3%. The VGG-16 is the highest with an accuracy of 94.3% and F1-score of 94.3%. The majority voting of the 18 models and the highest 4 models achieved an accuracy of 93.0% and 94.0%, respectively.

The proposed framework

In this section, the proposed framework has been explained. First, the used chest X-ray dataset has been described. Then, the developed framework, which includes “pre-processing” phase and the “Classification using CNN models based on transfer learning” phase, has been illustrated. Two different approaches have been used to train pre-trained CNN models using transfer learning. The first approach uses whole chest X-ray images, while the other approach uses lung-segmented images.

Used datasets

In this research, the data obtained from the “COVID-19 Radiography Database” has been used to apply the proposed framework. The database contains thousands of publicly available benchmark X-ray images and corresponding lung masks. The X-ray images are provided in Portable Network Graphics (PNG) format with a resolution of 299 × 299 pixels. The database includes 10,192 Normal cases, 3616 positive COVID-19 cases, 1345 Viral Pneumonia cases, and 6012 Lung Opacity images as shown in Table 1 . This database was developed by a team from Qatar University, Dhaka University, Bangladesh with cooperators from Malaysia and Pakistan and cooperators of medical doctors 26 . Figure  1 illustrates samples from different classes in the COVID-19 Radiography Database.

Samples from COVID-19 radiography chest database representing different classes.

Preprocessing

The purpose of the pre-processing phase is to prepare the X-ray images for classification using CNN pre-trained models. In this phase, different pre-processing steps are applied to improve the performance of the classification. The pre-processing steps can be summarized as follows:

Image enhancement

Enhancing images is a significant step for the correct classification. It increases image contrast in order to improve classification performance. Different techniques can be applied to enhance the images. In this research, some of these techniques have been applied to the original X-ray images before introducing them to the classification models, they are as follows:

Histogram Equalization (HE): The purpose of histogram equalization (HE) is to spread the gray levels inside the image. It modifies the brightness and contrast of the images to improve the image quality 27 . The original X-ray images’ intensity has been enhanced using histogram equalization (HE).

Contrast Limited Adaptive Histogram Equalization (CLAHE): It originated from Global Histogram Equalization (GHE), it is based on dividing the image into non-overlapping blocks, and after that, the histogram of each block is gotten using a pre-specified value 28 . In this research, CLAHE has been used to enhance the contrast of original X-ray images.

Image Complement: The complement or inverse of X-ray images transforms the dark positions to lighter and the light positions to darker. As this is a standard process, which is similar to that used by radiologists, it may aid a deep learning model for improving classification performance. The complement of the binary image can be obtained by changing the zeros to ones and ones to zeros. Whereas for a grayscale image, each pixel is subtracted from 255.

Figure  2 shows an original X-ray image and its enhanced versions after applying HE, CLAHE and image complement on the original image with the corresponding histogram plots for each version.

An X-ray image and its enhanced versions after applying HE, CLAHE and complement to the original image and the corresponding histogram plots.

Segmentation

In the segmentation step, the regions of interest (ROI), which are the lungs region in our case, are cropped from the associated image. In this research, the ground truth lungs’ masks which are provided by the database have been used. A modified U-Net model was applied by the authors of the database on the X-ray images to get the lung masks associated with the full X-ray images. In this research, multiplication between each original image and the associated lung mask has been applied to get the segmented lungs. The same process of multiplication between different enhanced image versions and the associated masks has been applied to get different versions of segmented datasets with different enhancements. All these versions are introduced to CNN models as segmented versions of data. Figure  3 shows the segmented images of the original image and of the different enhanced images for one of the COVID samples.

X-ray original image and its enhanced versions and the segmented lung region of each version.

Resizing images phase

Resizing the images is an essential process to satisfy the requirement of CNN of equally sized input images. In this research, the process of resizing X-ray images has been done to fit all X-ray images to the input size of the used pre-trained CNN models which are VGG19 and EfficientNetB0. Therefore, all images’ versions either full or segmented versions were resized to fit the CNNs input image size which is 224 × 224 pixels. To expedite the training process, it was found that the size of 112 × 112 pixels expedited the training without affecting the performance metrics.

Classification using pre-trained convolution neural network model

In this research, different versions of either full or segmented chest X-ray images have been introduced to CNN models to train the classifiers. Different experiments have been carried out on the original and segmented lung X-ray images both with their different enhanced versions. The classification has been done using VGG19 14 and EfficientNetB0 16 pre-trained CNN models. After the calculation of different performance metrics, the best model has been selected as the adopted model. The next subsections give a brief description of the used pre-trained models.

VGG19 model

VGG19 is a variant of the VGG CNN model which was created by Visual Geometry Group (VGG) at Oxford University. VGG19 was one of the winners of the Image Net Large Scale Visual Recognition Challenge (ILSVRC) in 2014. The size of the input image to VGG19 is (224 × 224). VGG19 contains 16 convolution layers, 5 max-pooling layers and 3 fully connected layers. The convolution layers are with (3 × 3) filters' size, stride of 1 pixel and padding of 1 pixel. The max-pooling layers are with a size of 2 × 2 and a stride of 2. The rectification (ReLU) activation function is utilized for all hidden layers. Then, the first 2 fully connected layers with 4096 channels each are uitilized followed by the last layer of 1000 channels to represent the different 1000 classes of the ImageNet with soft-max activation function 15 .

EfficientNetB0 model

Google research group designed a family of models, called EfficientNets using a scaling method and achieved better efficiency and accuracy than previous ConvNets. EfficientNet is based on scaling CNNs and reaching better performance by balancing network width, depth, and resolution. Therefore, the focus is to present a scaling method to uniformly scale the 3 dimensions with a simple highly effective compound coefficient. Thus, it can be considered as an optimization problem to find the best coefficients for depth, width, and resolution that maximizes the accuracy of the network given the constraints of the available resources. The primary building block of the EfficientNet models is MBConv. The network's dimension equation was used to get the family of neural networks EfficientNet-B0 to B7 16 . In this research, EfficientNetB0 was used for the classification of the chest X-ray images. Figure  4 sums up the framework of the adopted methodology in this research.

The framework of the used methodology for Chest X-ray images classification.

Ethical approval

This article does not contain any studies with human participants or animals performed by the author.

Experimental results

This section presents the experimental results of the proposed framework to evaluate its performance and study the effect of different enhancements applied to the X-ray images on the performance of the CNN classification model.

Experimental setup

Keras Python deep learning library on top of TensorFlow was utilized for implementing CNN models on a machine with the following specification; an Intel ® Core™ i7 CPU@ 3.6 GHz with 32 GB RAM and a Titan × Pascal Graphics Processing Unit (GPU). Extensive experiments were carried out to obtain the best settings of the CNN models that achieve the best possible results. It is worth noting that the pre-processing for enhancing the images has been carried out using MATLAB ® 18 software.

Both the full and segmented datasets with their enhanced versions have been used to train VGG19 and EfficientNetB0 CNN pre-trained models. The training was carried on with Adam optimizer, learning rate of 0.001, batch size of 32, and the number of epochs (10–30) epochs, SoftMax classifier. The fine-tuned pre-trained models were used for feature extraction; therefore, the weights of the pre-trained models were frozen, and they were not updated during the training to maintain ImageNet’s initial weights. The top layers were fine-tuned to adjust the network according to the used chest X-ray data and to the current problem output which is (2/4) rather than 1000 in the ImageNet data. To avoid overfitting, a dropout of 0.3 was applied in the fully connected layers. Figures  5 and 6 illustrate the fine-tuned top layers based on VGG19 and EfficientNetB0 respectively for binary classification.

Fine-tuned top layers based on VGG19 pre-training model.

Fine-tuned top layers based on EfficientNetB0 pre-training model.

Performance evaluation

A benchmark dataset was employed to validate the performance of the proposed framework. For binary classification, a set of 1600 X-ray images (800 of each class) have been used to train CNN models using transfer learning. The dataset has been divided into three subsets training, validation, and test sets. The training set is used for learning the model and adjusting the parameters. The validation set is to test the model during the training phase and fine-tune the parameters. The test set is to evaluate the trained model. The division was done as 400 samples (25%) of the used X-ray images were selected randomly for testing (200 images for each class), and the remaining 75% samples were split again into training and validation splits (80–20%). For 4-classes classification, a set of 3200 X-ray images (800 of each class) have been used. Before training CNN models, different pre-processing steps were implemented to enhance the images of both full and segmented lungs chest X-ray images to investigate the classification performance of the CNN models using the different versions.

The following metrics were used for the evaluation of the different CNN models trained using various dataset versions:

where TP is a true-positive value, FP is a false-positive value, TN is a true-negative value and FN is a false-negative value.

Tables 2 and 3 show the results of training VGG19 using the original and different enhanced versions of full X-ray images and of segmented versions respectively. As it is shown in Table 2 , applying different image enhancement techniques has improved the performance of the classification model. The accuracy of classification for the model trained using the original images’ version was 0.913, however, it has been improved for the enhanced versions to reach 0.94, 0.95, and 0.9475 for histeq, CLAHE, and complement respectively. The detailed results including TP, TN, FP, FN, sensitivity, specificity, precision, F1 score, test accuracy, and AUC of the proposed full X-ray images using VGG19 are shown in Table 2 . The performance of the model trained using the CLAHE version is the best.

Regarding the segmented versions, as it is shown in Table 3 the accuracy of classification of the model trained using the original segmented dataset version was 0.887. However, the accuracy has been improved for the enhanced segmented dataset versions to reach 0.91 using Histeq techniques, 0.9049 for CLAHE and 0.9075 for the complement version. It is clear that the accuracies using different enhanced versions are close to each other, and they are better than that of the original segmented version. The detailed results using the different metrics are shown in Table 3 .

Table 4 shows the results of training EfficientNetB0 using the original and different enhanced versions of full X-ray images. The accuracy of classification using the original full images’ version was 0.915, it reached 0.94, 0.938, and 0.94 for histeq, CLAHE, and complement versions respectively. The accuracies for the models trained using different enhanced versions are better than that of the original version. The detailed results using the different metrics are shown in Table 4 .

Regarding the segmented versions, as shown in Table 5 the accuracy of the classification for the EfficientNetB0 model trained using the original segmented lung dataset version was 0.885. However, the accuracy has been improved to 0.905, 0.905, and 0.9075 for Histeq, CLAHE and complement versions respectively. As with VGG19, the accuracies of training EfficientNetB0 using different enhanced segmented versions are close to each other, but they are all better than that of the original segmented version. The detailed results using the different metrics are shown in Table 5 .

It is clear that the performance of all enhanced versions is better than that of their associated original version using either VGG19 or EfficientNetB0 models and for both full and segmented versions. By comparing the results of the full X-ray images and segmented images using the same CNN model, reductions in the performance of the CNN models trained using the segmented datasets rather than those trained using full-image datasets were observed. The models built using full images achieved better performance in general. The reason for that might be that the full images may have more details outside the lung region in the surroundings region that contribute to the classification and help to improve the performance.

Regarding the comparison of the used 2 CNNs models, the results of the two models are close to each other, however, the results of VGG19 are a bit better than those of EfficientNetB0. The best achieved performance is of the VGG19 model trained using the CLAHE version of full X-ray images for binary classification of chest X-ray images into COVID-19 or normal. It achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95. Figures  7 , 8 , 9 and 10 show the training and validation accuracies, the training and validation losses, the training and test ROC curve and the confusion matrix of that best achieved model respectively.

Training and validation accuracy of the best model.

Training and validation losses of the best model.

Training and test ROC curve of the best achieved model.

Confusion matrix of the best model.

Distinguishing COVID-19 from normal and other classes is one of the important issues since the pandemic in 2019. The contribution of this research is to develop a framework to classify Coronavirus suspected cases as normal or COVID-19 positive cases. Different pre-processing steps have been applied to improve the performance of the classification. After that, multiplication between the original images and the associated lung masks has been applied to get the segmented lungs. The same process of multiplication has been applied between different enhanced image versions and the associated masks to get different enhanced versions of segmented datasets. All these versions are introduced to CNN models which are VGG19 and EfficientNetB0. Therefore, two different approaches have been used to train pre-trained CNN models using transfer learning. The first approach uses full chest X-ray images, while the other approach uses lung segmented images.

From the results of conducted experiments, it has been observed that the proposed framework has achieved a good performance using either full or segmented images, however the performance using full images is better than that using segmented. Moreover, it has been observed that the performance of the classification models has been improved after applying enhancement techniques.

To evaluate the proposed framework with respect to the state-of-the-art works in COVID-19, it has been compared with the related works reviewed in this research as described in Table 6 . It is worth mentioning that, the comparison is not an easy task in COVID research as the pandemic broke out in the world suddenly, all Covid research used different sources of data either local, public, or combined from different databases. Some of the public datasets are collected from different other databases. Even the research that used the same public datasets used different number of samples. Some research performed binary class classification, where others performed multi-class classification. Thus, the proposed work has been compared with others that used the same modality which is X-ray with mentioning the number of used samples and the task.

By comparing the results of the proposed framework with recent literature, it was found that the proposed framework outperforms most of the state-of-the-art works. However, 21 slightly outperforms the proposed framework. Where the accuracy and F1-score of 21 are 96.71% and 97% respectively, the corresponding values of the proposed framework are 95% and 0.9505 respectively. Taking in consideration that in 21 un-balanced data has been used; the number of COVID images used is 3626 where the number of normal images is 10,198 as mentioned in the manuscript.

For more validation, different classes of the datasets have been used for training the CNN models. To check the capability of the proposed framework for 4-classes classification. The dataset versions that achieved the highest binary classification have been utilized for multi-classes classification. Since the performance of all models that trained using enhanced full image versions is close to each other, therefore, these versions have been utilized for 4-classes classification. A set of 3200 X-ray images (800 of each class) have been used to train CNN models. The newly added classes are Viral Pneumonia and Lung Opacity in addition to COVID-19 and normal classes.

It was found that the best-achieved accuracy of 4-classes classification using the full image versions reached 0.935 for histeq version by EfficientNetB0. While it reached 0.93375 for both CLAHE, and complement versions using VGG19. Table 7 shows the results of the best-achieved models for 4-classes classification.

Conclusion and future work

In this research, a framework has been developed for automatically classifying chest X-ray images as COVID-19 positive cases or normal cases. Different techniques such as histeq, CLAHE, and complement have been applied to enhance the original X-ray images and therefore, both the original and enhanced versions have been introduced to the selected CNN pre-trained models. Two pre-trained CNN models which are VGG19 and EfficientNetB0 have been used to train different versions with the last dense layer set to (2/4) according to the number of classification classes.

Two approaches have been utilized to train pre-trained CNN models which are using whole chest X-ray images and using lung segmented images with their enhanced versions. The best binary classification accuracy reached 95% for the model trained using CLAHE full images version utilizing VGG19. The best achieved accuracy for a model trained using a segmented dataset is 91% for the model trained using Histeq version utilizing VGG19. By testing the framework for 4-classes classification, it achieved promising results which reached 0.935 accuracy.

It is obvious from the results that, the proposed framework can be employed in the future to support physicians and decrease the effect of doctors’ shortages in the struggle against the disease. However, extra validations are required before applying any system, as more accuracy and more careful experiments are needed when things are related to human life. In the future, the authors are willing to try the proposed model on local data.

Data availability

The used data has been obtained from an available online database and it has been referenced in the manuscript. The link to the database used in the study: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database

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El Houby, E.M.F. COVID‑19 detection from chest X-ray images using transfer learning. Sci Rep 14 , 11639 (2024). https://doi.org/10.1038/s41598-024-61693-0

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DOI : https://doi.org/10.1038/s41598-024-61693-0

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