Next Article in Journal
Incorporating Patient Preferences into a Decision-Making Model of Hand Trauma Reconstruction
Next Article in Special Issue
Driver, Collision and Meteorological Characteristics of Motor Vehicle Collisions among Road Trauma Survivors
Previous Article in Journal
Ventilation and Pollutant Concentration for the Pedestrian Zone, the Near-Wall Zone, and the Canopy Layer at Urban Intersections
Previous Article in Special Issue
Item Analysis of the Czech Version of the WJ IV COG Battery from a Group of Romani Children
Article

COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare

1
School of Electronics Engineering, KIIT Deemed to be University, Odisha 751024, India
2
Department of Computer Science & Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad 501218, India
3
Department of Computer Science, Aalto University, 02150 Espoo, Finland
4
Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Software, Sejong University, Seoul 143-747, Korea
5
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
6
Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450001, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Alireza Daneshkhah, Amin Hosseinian-Far, Samer A. Kharroubi and Vasile Palade
Int. J. Environ. Res. Public Health 2021, 18(21), 11086; https://doi.org/10.3390/ijerph182111086
Received: 23 September 2021 / Revised: 16 October 2021 / Accepted: 17 October 2021 / Published: 21 October 2021
(This article belongs to the Special Issue Data Science for Environment and Health Applications)
In the recent pandemic, accurate and rapid testing of patients remained a critical task in the diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of the sudden increase in cases, most countries have faced scarcity and a low rate of testing. Chest X-rays have been shown in the literature to be a potential source of testing for COVID-19 patients, but manually checking X-ray reports is time-consuming and error-prone. Considering these limitations and the advancements in data science, we proposed a Vision Transformer-based deep learning pipeline for COVID-19 detection from chest X-ray-based imaging. Due to the lack of large data sets, we collected data from three open-source data sets of chest X-ray images and aggregated them to form a 30 K image data set, which is the largest publicly available collection of chest X-ray images in this domain to our knowledge. Our proposed transformer model effectively differentiates COVID-19 from normal chest X-rays with an accuracy of 98% along with an AUC score of 99% in the binary classification task. It distinguishes COVID-19, normal, and pneumonia patient’s X-rays with an accuracy of 92% and AUC score of 98% in the Multi-class classification task. For evaluation on our data set, we fine-tuned some of the widely used models in literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, and DenseNet-121, as baselines. Our proposed transformer model outperformed them in terms of all metrics. In addition, a Grad-CAM based visualization is created which makes our approach interpretable by radiologists and can be used to monitor the progression of the disease in the affected lungs, assisting healthcare. View Full-Text
Keywords: vision transformer; COVID-19; deep learning; data science; healthcare; interpretability; transfer learning; grad-CAM vision transformer; COVID-19; deep learning; data science; healthcare; interpretability; transfer learning; grad-CAM
Show Figures

Figure 1

MDPI and ACS Style

Shome, D.; Kar, T.; Mohanty, S.N.; Tiwari, P.; Muhammad, K.; AlTameem, A.; Zhang, Y.; Saudagar, A.K.J. COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare. Int. J. Environ. Res. Public Health 2021, 18, 11086. https://doi.org/10.3390/ijerph182111086

AMA Style

Shome D, Kar T, Mohanty SN, Tiwari P, Muhammad K, AlTameem A, Zhang Y, Saudagar AKJ. COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare. International Journal of Environmental Research and Public Health. 2021; 18(21):11086. https://doi.org/10.3390/ijerph182111086

Chicago/Turabian Style

Shome, Debaditya, T. Kar, Sachi N. Mohanty, Prayag Tiwari, Khan Muhammad, Abdullah AlTameem, Yazhou Zhang, and Abdul K.J. Saudagar 2021. "COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare" International Journal of Environmental Research and Public Health 18, no. 21: 11086. https://doi.org/10.3390/ijerph182111086

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop