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

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 sh...

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Autores principales: Shome, Debaditya, Kar, T., Mohanty, Sachi Nandan, Tiwari, Prayag, Muhammad, Khan, AlTameem, Abdullah, Zhang, Yazhou, Saudagar, Abdul Khader Jilani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8583247/
https://www.ncbi.nlm.nih.gov/pubmed/34769600
http://dx.doi.org/10.3390/ijerph182111086
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author Shome, Debaditya
Kar, T.
Mohanty, Sachi Nandan
Tiwari, Prayag
Muhammad, Khan
AlTameem, Abdullah
Zhang, Yazhou
Saudagar, Abdul Khader Jilani
author_facet Shome, Debaditya
Kar, T.
Mohanty, Sachi Nandan
Tiwari, Prayag
Muhammad, Khan
AlTameem, Abdullah
Zhang, Yazhou
Saudagar, Abdul Khader Jilani
author_sort Shome, Debaditya
collection PubMed
description 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.
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spelling pubmed-85832472021-11-12 COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare Shome, Debaditya Kar, T. Mohanty, Sachi Nandan Tiwari, Prayag Muhammad, Khan AlTameem, Abdullah Zhang, Yazhou Saudagar, Abdul Khader Jilani Int J Environ Res Public Health Article 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. MDPI 2021-10-21 /pmc/articles/PMC8583247/ /pubmed/34769600 http://dx.doi.org/10.3390/ijerph182111086 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shome, Debaditya
Kar, T.
Mohanty, Sachi Nandan
Tiwari, Prayag
Muhammad, Khan
AlTameem, Abdullah
Zhang, Yazhou
Saudagar, Abdul Khader Jilani
COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare
title COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare
title_full COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare
title_fullStr COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare
title_full_unstemmed COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare
title_short COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare
title_sort covid-transformer: interpretable covid-19 detection using vision transformer for healthcare
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8583247/
https://www.ncbi.nlm.nih.gov/pubmed/34769600
http://dx.doi.org/10.3390/ijerph182111086
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