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:	Debaditya Shome, T. Kar, Sachi Nandan Mohanty, Prayag Tiwari, Khan Muhammad, Abdullah AlTameem, Yazhou Zhang, Abdul Khader Jilani Saudagar
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	vision transformer COVID-19 deep learning data science healthcare interpretability Medicine R
Acceso en línea:	https://doaj.org/article/95911419892447af80672c03a79fc753
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id	oai:doaj.org-article:95911419892447af80672c03a79fc753
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spelling	oai:doaj.org-article:95911419892447af80672c03a79fc7532021-11-11T16:13:36ZCOVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare10.3390/ijerph1821110861660-46011661-7827https://doaj.org/article/95911419892447af80672c03a79fc7532021-10-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/21/11086https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601In 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.Debaditya ShomeT. KarSachi Nandan MohantyPrayag TiwariKhan MuhammadAbdullah AlTameemYazhou ZhangAbdul Khader Jilani SaudagarMDPI AGarticlevision transformerCOVID-19deep learningdata sciencehealthcareinterpretabilityMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 11086, p 11086 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	vision transformer COVID-19 deep learning data science healthcare interpretability Medicine R
spellingShingle	vision transformer COVID-19 deep learning data science healthcare interpretability Medicine R Debaditya Shome T. Kar Sachi Nandan Mohanty Prayag Tiwari Khan Muhammad Abdullah AlTameem Yazhou Zhang Abdul Khader Jilani Saudagar COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare
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.
format	article
author	Debaditya Shome T. Kar Sachi Nandan Mohanty Prayag Tiwari Khan Muhammad Abdullah AlTameem Yazhou Zhang Abdul Khader Jilani Saudagar
author_facet	Debaditya Shome T. Kar Sachi Nandan Mohanty Prayag Tiwari Khan Muhammad Abdullah AlTameem Yazhou Zhang Abdul Khader Jilani Saudagar
author_sort	Debaditya Shome
title	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_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_sort	covid-transformer: interpretable covid-19 detection using vision transformer for healthcare
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/95911419892447af80672c03a79fc753
work_keys_str_mv	AT debadityashome covidtransformerinterpretablecovid19detectionusingvisiontransformerforhealthcare AT tkar covidtransformerinterpretablecovid19detectionusingvisiontransformerforhealthcare AT sachinandanmohanty covidtransformerinterpretablecovid19detectionusingvisiontransformerforhealthcare AT prayagtiwari covidtransformerinterpretablecovid19detectionusingvisiontransformerforhealthcare AT khanmuhammad covidtransformerinterpretablecovid19detectionusingvisiontransformerforhealthcare AT abdullahaltameem covidtransformerinterpretablecovid19detectionusingvisiontransformerforhealthcare AT yazhouzhang covidtransformerinterpretablecovid19detectionusingvisiontransformerforhealthcare AT abdulkhaderjilanisaudagar covidtransformerinterpretablecovid19detectionusingvisiontransformerforhealthcare
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COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare

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