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Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images
COVID-19 has been a global pandemic. Flattening the curve requires intensive testing, and the world has been facing a shortage of testing equipment and medical personnel with expertise. There is a need to automate and aid the detection process. Several diagnostic tools are currently being used for C...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10105354/ https://www.ncbi.nlm.nih.gov/pubmed/37089895 http://dx.doi.org/10.1007/s42979-022-01655-3 |
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author | Agrawal, Shubham Honnakasturi, Venkatesh Nara, Madhumitha Patil, Nagamma |
author_facet | Agrawal, Shubham Honnakasturi, Venkatesh Nara, Madhumitha Patil, Nagamma |
author_sort | Agrawal, Shubham |
collection | PubMed |
description | COVID-19 has been a global pandemic. Flattening the curve requires intensive testing, and the world has been facing a shortage of testing equipment and medical personnel with expertise. There is a need to automate and aid the detection process. Several diagnostic tools are currently being used for COVID-19, including X-Rays and CT-scans. This study focuses on detecting COVID-19 from X-Rays. We pursue two types of problems: binary classification (COVID-19 and No COVID-19) and multi-class classification (COVID-19, No COVID-19 and Pneumonia). We examine and evaluate several classic models, namely VGG19, ResNet50, MobileNetV2, InceptionV3, Xception, DenseNet121, and specialized models such as DarkCOVIDNet and COVID-Net and prove that ResNet50 models perform best. We also propose a simple modification to the ResNet50 model, which gives a binary classification accuracy of 99.20% and a multi-class classification accuracy of 86.13%, hence cementing the ResNet50’s abilities for COVID-19 detection and ability to differentiate pneumonia and COVID-19. The proposed model’s explanations were interpreted via LIME which provides contours, and Grad-CAM, which provides heat-maps over the area(s) of interest of the classifier, i.e., COVID-19 concentrated regions in the lungs, and realize that LIME explains the results better. These explanations support our model’s ability to generalize. The proposed model is intended to be deployed for free use. |
format | Online Article Text |
id | pubmed-10105354 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-101053542023-04-17 Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images Agrawal, Shubham Honnakasturi, Venkatesh Nara, Madhumitha Patil, Nagamma SN Comput Sci Original Research COVID-19 has been a global pandemic. Flattening the curve requires intensive testing, and the world has been facing a shortage of testing equipment and medical personnel with expertise. There is a need to automate and aid the detection process. Several diagnostic tools are currently being used for COVID-19, including X-Rays and CT-scans. This study focuses on detecting COVID-19 from X-Rays. We pursue two types of problems: binary classification (COVID-19 and No COVID-19) and multi-class classification (COVID-19, No COVID-19 and Pneumonia). We examine and evaluate several classic models, namely VGG19, ResNet50, MobileNetV2, InceptionV3, Xception, DenseNet121, and specialized models such as DarkCOVIDNet and COVID-Net and prove that ResNet50 models perform best. We also propose a simple modification to the ResNet50 model, which gives a binary classification accuracy of 99.20% and a multi-class classification accuracy of 86.13%, hence cementing the ResNet50’s abilities for COVID-19 detection and ability to differentiate pneumonia and COVID-19. The proposed model’s explanations were interpreted via LIME which provides contours, and Grad-CAM, which provides heat-maps over the area(s) of interest of the classifier, i.e., COVID-19 concentrated regions in the lungs, and realize that LIME explains the results better. These explanations support our model’s ability to generalize. The proposed model is intended to be deployed for free use. Springer Nature Singapore 2023-04-15 2023 /pmc/articles/PMC10105354/ /pubmed/37089895 http://dx.doi.org/10.1007/s42979-022-01655-3 Text en © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Agrawal, Shubham Honnakasturi, Venkatesh Nara, Madhumitha Patil, Nagamma Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images |
title | Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images |
title_full | Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images |
title_fullStr | Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images |
title_full_unstemmed | Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images |
title_short | Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images |
title_sort | utilizing deep learning models and transfer learning for covid-19 detection from x-ray images |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10105354/ https://www.ncbi.nlm.nih.gov/pubmed/37089895 http://dx.doi.org/10.1007/s42979-022-01655-3 |
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