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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8583247 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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