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COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence

COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise o...

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Autores principales: Khan, Muhammad Attique, Azhar, Marium, Ibrar, Kainat, Alqahtani, Abdullah, Alsubai, Shtwai, Binbusayyis, Adel, Kim, Ye Jin, Chang, Byoungchol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9284325/
https://www.ncbi.nlm.nih.gov/pubmed/35845911
http://dx.doi.org/10.1155/2022/4254631
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author Khan, Muhammad Attique
Azhar, Marium
Ibrar, Kainat
Alqahtani, Abdullah
Alsubai, Shtwai
Binbusayyis, Adel
Kim, Ye Jin
Chang, Byoungchol
author_facet Khan, Muhammad Attique
Azhar, Marium
Ibrar, Kainat
Alqahtani, Abdullah
Alsubai, Shtwai
Binbusayyis, Adel
Kim, Ye Jin
Chang, Byoungchol
author_sort Khan, Muhammad Attique
collection PubMed
description COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.
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spelling pubmed-92843252022-07-16 COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence Khan, Muhammad Attique Azhar, Marium Ibrar, Kainat Alqahtani, Abdullah Alsubai, Shtwai Binbusayyis, Adel Kim, Ye Jin Chang, Byoungchol Comput Intell Neurosci Research Article COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods. Hindawi 2022-07-14 /pmc/articles/PMC9284325/ /pubmed/35845911 http://dx.doi.org/10.1155/2022/4254631 Text en Copyright © 2022 Muhammad Attique Khan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Khan, Muhammad Attique
Azhar, Marium
Ibrar, Kainat
Alqahtani, Abdullah
Alsubai, Shtwai
Binbusayyis, Adel
Kim, Ye Jin
Chang, Byoungchol
COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence
title COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence
title_full COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence
title_fullStr COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence
title_full_unstemmed COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence
title_short COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence
title_sort covid-19 classification from chest x-ray images: a framework of deep explainable artificial intelligence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9284325/
https://www.ncbi.nlm.nih.gov/pubmed/35845911
http://dx.doi.org/10.1155/2022/4254631
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