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An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network
Researchers have developed more intelligent, highly responsive, and efficient detection methods owing to the COVID-19 demands for more widespread diagnosis. The work done deals with developing an AI-based framework that can help radiologists and other healthcare professionals diagnose COVID-19 cases...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906898/ https://www.ncbi.nlm.nih.gov/pubmed/35290811 http://dx.doi.org/10.1016/j.compbiomed.2022.105383 |
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author | Baghdadi, Nadiah A. Malki, Amer Abdelaliem, Sally F. Magdy Balaha, Hossam Badawy, Mahmoud Elhosseini, Mostafa |
author_facet | Baghdadi, Nadiah A. Malki, Amer Abdelaliem, Sally F. Magdy Balaha, Hossam Badawy, Mahmoud Elhosseini, Mostafa |
author_sort | Baghdadi, Nadiah A. |
collection | PubMed |
description | Researchers have developed more intelligent, highly responsive, and efficient detection methods owing to the COVID-19 demands for more widespread diagnosis. The work done deals with developing an AI-based framework that can help radiologists and other healthcare professionals diagnose COVID-19 cases at a high level of accuracy. However, in the absence of publicly available CT datasets, the development of such AI tools can prove challenging. Therefore, an algorithm for performing automatic and accurate COVID-19 classification using Convolutional Neural Network (CNN), pre-trained model, and Sparrow search algorithm (SSA) on CT lung images was proposed. The pre-trained CNN models used are SeresNext50, SeresNext101, SeNet154, MobileNet, MobileNetV2, MobileNetV3Small, and MobileNetV3Large. In addition, the SSA will be used to optimize the different CNN and transfer learning(TL) hyperparameters to find the best configuration for the pre-trained model used and enhance its performance. Two datasets are used in the experiments. There are two classes in the first dataset, while three in the second. The authors combined two publicly available COVID-19 datasets as the first dataset, namely the COVID-19 Lung CT Scans and COVID-19 CT Scan Dataset. In total, 14,486 images were included in this study. The authors analyzed the Large COVID-19 CT scan slice dataset in the second dataset, which utilized 17,104 images. Compared to other pre-trained models on both classes datasets, MobileNetV3Large pre-trained is the best model. As far as the three-classes dataset is concerned, a model trained on SeNet154 is the best available. Results show that, when compared to other CNN models like LeNet-5 CNN, COVID faster R–CNN, Light CNN, Fuzzy + CNN, Dynamic CNN, CNN and Optimized CNN, the proposed Framework achieves the best accuracy of 99.74% (two classes) and 98% (three classes). |
format | Online Article Text |
id | pubmed-8906898 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89068982022-03-10 An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network Baghdadi, Nadiah A. Malki, Amer Abdelaliem, Sally F. Magdy Balaha, Hossam Badawy, Mahmoud Elhosseini, Mostafa Comput Biol Med Article Researchers have developed more intelligent, highly responsive, and efficient detection methods owing to the COVID-19 demands for more widespread diagnosis. The work done deals with developing an AI-based framework that can help radiologists and other healthcare professionals diagnose COVID-19 cases at a high level of accuracy. However, in the absence of publicly available CT datasets, the development of such AI tools can prove challenging. Therefore, an algorithm for performing automatic and accurate COVID-19 classification using Convolutional Neural Network (CNN), pre-trained model, and Sparrow search algorithm (SSA) on CT lung images was proposed. The pre-trained CNN models used are SeresNext50, SeresNext101, SeNet154, MobileNet, MobileNetV2, MobileNetV3Small, and MobileNetV3Large. In addition, the SSA will be used to optimize the different CNN and transfer learning(TL) hyperparameters to find the best configuration for the pre-trained model used and enhance its performance. Two datasets are used in the experiments. There are two classes in the first dataset, while three in the second. The authors combined two publicly available COVID-19 datasets as the first dataset, namely the COVID-19 Lung CT Scans and COVID-19 CT Scan Dataset. In total, 14,486 images were included in this study. The authors analyzed the Large COVID-19 CT scan slice dataset in the second dataset, which utilized 17,104 images. Compared to other pre-trained models on both classes datasets, MobileNetV3Large pre-trained is the best model. As far as the three-classes dataset is concerned, a model trained on SeNet154 is the best available. Results show that, when compared to other CNN models like LeNet-5 CNN, COVID faster R–CNN, Light CNN, Fuzzy + CNN, Dynamic CNN, CNN and Optimized CNN, the proposed Framework achieves the best accuracy of 99.74% (two classes) and 98% (three classes). Elsevier Ltd. 2022-05 2022-03-10 /pmc/articles/PMC8906898/ /pubmed/35290811 http://dx.doi.org/10.1016/j.compbiomed.2022.105383 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Baghdadi, Nadiah A. Malki, Amer Abdelaliem, Sally F. Magdy Balaha, Hossam Badawy, Mahmoud Elhosseini, Mostafa An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network |
title | An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network |
title_full | An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network |
title_fullStr | An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network |
title_full_unstemmed | An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network |
title_short | An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network |
title_sort | automated diagnosis and classification of covid-19 from chest ct images using a transfer learning-based convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906898/ https://www.ncbi.nlm.nih.gov/pubmed/35290811 http://dx.doi.org/10.1016/j.compbiomed.2022.105383 |
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