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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9963211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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