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

Author

Listed:
  • Debaditya Shome

    (School of Electronics Engineering, KIIT Deemed to be University, Odisha 751024, India)

  • T. Kar

    (School of Electronics Engineering, KIIT Deemed to be University, Odisha 751024, India)

  • Sachi Nandan Mohanty

    (Department of Computer Science & Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad 501218, India)

  • Prayag Tiwari

    (Department of Computer Science, Aalto University, 02150 Espoo, Finland)

  • Khan Muhammad

    (Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Korea)

  • Abdullah AlTameem

    (Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Yazhou Zhang

    (Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450001, China)

  • Abdul Khader Jilani Saudagar

    (Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

Abstract

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.

Suggested Citation

  • Debaditya Shome & T. Kar & Sachi Nandan Mohanty & Prayag Tiwari & Khan Muhammad & Abdullah AlTameem & Yazhou Zhang & Abdul Khader Jilani Saudagar, 2021. "COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare," IJERPH, MDPI, vol. 18(21), pages 1-14, October.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11086-:d:661711
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    References listed on IDEAS

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    1. Abhinav Vepa & Amer Saleem & Kambiz Rakhshan & Alireza Daneshkhah & Tabassom Sedighi & Shamarina Shohaimi & Amr Omar & Nader Salari & Omid Chatrabgoun & Diana Dharmaraj & Junaid Sami & Shital Parekh &, 2021. "Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients," IJERPH, MDPI, vol. 18(12), pages 1-22, June.
    2. Trudie Lang, 2020. "Plug COVID-19 research gaps in detection, prevention and care," Nature, Nature, vol. 583(7816), pages 333-333, July.
    3. Anna L. Cass & Meghan M. Slining & Connie Carson & Jason Cassidy & M. Carmela Epright & Ann E. Gilchrist & Kenneth Peterson & John F. Wheeler & Natalie S. The, 2021. "Risk Management of COVID-19 in the Residential Educational Setting: Lessons Learned and Implications for Moving Forward," IJERPH, MDPI, vol. 18(18), pages 1-13, September.
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    Cited by:

    1. Lan Huang & Yanli Qu & Kai He & Yan Wang & Dan Shao, 2022. "DenSec: Secreted Protein Prediction in Cerebrospinal Fluid Based on DenseNet and Transformer," Mathematics, MDPI, vol. 10(14), pages 1-10, July.

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