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Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation

A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection’s progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT...

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Autores principales: Khan, Hassan Ali, Gong, Xueqing, Bi, Fenglin, Ali, Rashid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9963211/
https://www.ncbi.nlm.nih.gov/pubmed/36826961
http://dx.doi.org/10.3390/jimaging9020042
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author Khan, Hassan Ali
Gong, Xueqing
Bi, Fenglin
Ali, Rashid
author_facet Khan, Hassan Ali
Gong, Xueqing
Bi, Fenglin
Ali, Rashid
author_sort Khan, Hassan Ali
collection PubMed
description A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection’s progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT) scans and chest X-ray radiographs, are frequently employed. The potential of artificial intelligence (AI) approaches further explored the creation of automated and precise COVID-19 detection systems. Scientists widely use deep learning techniques to identify coronavirus infection in lung imaging. In our paper, we developed a novel light CNN model architecture with watershed-based region-growing segmentation on Chest X-rays. Both CT scans and X-ray radiographs were employed along with 5-fold cross-validation. Compared to earlier state-of-the-art models, our model is lighter and outperformed the previous methods by achieving a mean accuracy of 98.8% on X-ray images and 98.6% on CT scans, predicting the rate of 0.99% and 0.97% for PPV (Positive predicted Value) and NPV (Negative predicted Value) rate of 0.98% and 0.99%, respectively.
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spelling pubmed-99632112023-02-26 Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation Khan, Hassan Ali Gong, Xueqing Bi, Fenglin Ali, Rashid J Imaging Article A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection’s progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT) scans and chest X-ray radiographs, are frequently employed. The potential of artificial intelligence (AI) approaches further explored the creation of automated and precise COVID-19 detection systems. Scientists widely use deep learning techniques to identify coronavirus infection in lung imaging. In our paper, we developed a novel light CNN model architecture with watershed-based region-growing segmentation on Chest X-rays. Both CT scans and X-ray radiographs were employed along with 5-fold cross-validation. Compared to earlier state-of-the-art models, our model is lighter and outperformed the previous methods by achieving a mean accuracy of 98.8% on X-ray images and 98.6% on CT scans, predicting the rate of 0.99% and 0.97% for PPV (Positive predicted Value) and NPV (Negative predicted Value) rate of 0.98% and 0.99%, respectively. MDPI 2023-02-13 /pmc/articles/PMC9963211/ /pubmed/36826961 http://dx.doi.org/10.3390/jimaging9020042 Text en © 2023 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
Khan, Hassan Ali
Gong, Xueqing
Bi, Fenglin
Ali, Rashid
Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation
title Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation
title_full Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation
title_fullStr Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation
title_full_unstemmed Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation
title_short Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation
title_sort novel light convolutional neural network for covid detection with watershed based region growing segmentation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9963211/
https://www.ncbi.nlm.nih.gov/pubmed/36826961
http://dx.doi.org/10.3390/jimaging9020042
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