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Predicting pattern of coronavirus using X-ray and CT scan images

Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world’s central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reac...

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Autores principales: Khurana Batra, Payal, Aggarwal, Paras, Wadhwa, Dheeraj, Gulati, Mehul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532815/
https://www.ncbi.nlm.nih.gov/pubmed/36212780
http://dx.doi.org/10.1007/s13721-022-00382-2
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author Khurana Batra, Payal
Aggarwal, Paras
Wadhwa, Dheeraj
Gulati, Mehul
author_facet Khurana Batra, Payal
Aggarwal, Paras
Wadhwa, Dheeraj
Gulati, Mehul
author_sort Khurana Batra, Payal
collection PubMed
description Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world’s central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slow in the process. To keep the potential virus carriers and potential causes of the disease quarantined as early as possible, there is still a requirement for a much faster and more accurate diagnostic process to supplement RT-PCR test of finding the patients affected by the virus. In this regard, radiological images such as X-ray and CT (Computerized Tomography) scan are found to be useful. The X-ray and CT scan have good screening modality; they are quick at capturing and finding and widely available around the world. Therefore, a deep learning model, which makes use of CT scan and X-ray images, has been proposed to automate and analyze the diagnostic process by utilizing Convolutional Neural Network (CNN). This model makes use of InceptionV3 deep learning model, a type of CNN. It is a lightweight deep learning model that is apt for mobile, laptop, and tablet platforms. The proposed model requires low memory space and gives an accuracy of about 96%, sensitivity of 93.48% for CXRs (Chest X-rays) and accuracy of 93%, sensitivity of 89.81 % for the CT scan images respectively. The proposed model is also compared with other deep learning models like VGG 16 (Visual Geometry Group), ResNet50V2 (Residual Network) and other existing deep learning models and it is found to be better in terms of accuracy and other performance parameters. Further, a web application has been developed from the proposed model. The web application is able to detect COVID-19 cases from the CT scan and X-ray images with significant accuracy.
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spelling pubmed-95328152022-10-05 Predicting pattern of coronavirus using X-ray and CT scan images Khurana Batra, Payal Aggarwal, Paras Wadhwa, Dheeraj Gulati, Mehul Netw Model Anal Health Inform Bioinform Original Article Novel coronavirus is a disease that can propagate easily with very minute carelessness and with very little physical contact between people. Presently, the world’s central health institution called the World Health Organization has approved and advised the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) swab test as the most important and effective diagnostic method to confirm if a patient has COVID-19 symptoms or not. This test takes at least a day for revealing the results, depending on the feasible resources in the neighborhood. Moreover, the RT-PCR test gives sometimes false positive results and slow in the process. To keep the potential virus carriers and potential causes of the disease quarantined as early as possible, there is still a requirement for a much faster and more accurate diagnostic process to supplement RT-PCR test of finding the patients affected by the virus. In this regard, radiological images such as X-ray and CT (Computerized Tomography) scan are found to be useful. The X-ray and CT scan have good screening modality; they are quick at capturing and finding and widely available around the world. Therefore, a deep learning model, which makes use of CT scan and X-ray images, has been proposed to automate and analyze the diagnostic process by utilizing Convolutional Neural Network (CNN). This model makes use of InceptionV3 deep learning model, a type of CNN. It is a lightweight deep learning model that is apt for mobile, laptop, and tablet platforms. The proposed model requires low memory space and gives an accuracy of about 96%, sensitivity of 93.48% for CXRs (Chest X-rays) and accuracy of 93%, sensitivity of 89.81 % for the CT scan images respectively. The proposed model is also compared with other deep learning models like VGG 16 (Visual Geometry Group), ResNet50V2 (Residual Network) and other existing deep learning models and it is found to be better in terms of accuracy and other performance parameters. Further, a web application has been developed from the proposed model. The web application is able to detect COVID-19 cases from the CT scan and X-ray images with significant accuracy. Springer Vienna 2022-10-05 2022 /pmc/articles/PMC9532815/ /pubmed/36212780 http://dx.doi.org/10.1007/s13721-022-00382-2 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022, Springer Nature or its licensor 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 Article
Khurana Batra, Payal
Aggarwal, Paras
Wadhwa, Dheeraj
Gulati, Mehul
Predicting pattern of coronavirus using X-ray and CT scan images
title Predicting pattern of coronavirus using X-ray and CT scan images
title_full Predicting pattern of coronavirus using X-ray and CT scan images
title_fullStr Predicting pattern of coronavirus using X-ray and CT scan images
title_full_unstemmed Predicting pattern of coronavirus using X-ray and CT scan images
title_short Predicting pattern of coronavirus using X-ray and CT scan images
title_sort predicting pattern of coronavirus using x-ray and ct scan images
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532815/
https://www.ncbi.nlm.nih.gov/pubmed/36212780
http://dx.doi.org/10.1007/s13721-022-00382-2
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