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A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

BACKGROUND AND OBJECTIVES: The traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether it is COVID-19 disease, which is easy to cause identification mistakes. In...

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Autores principales: Huang, Mei-Ling, Liao, Yu-Chieh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9090861/
https://www.ncbi.nlm.nih.gov/pubmed/35576824
http://dx.doi.org/10.1016/j.compbiomed.2022.105604
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author Huang, Mei-Ling
Liao, Yu-Chieh
author_facet Huang, Mei-Ling
Liao, Yu-Chieh
author_sort Huang, Mei-Ling
collection PubMed
description BACKGROUND AND OBJECTIVES: The traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether it is COVID-19 disease, which is easy to cause identification mistakes. In this study, the technology of convolutional neural network is expected to be able to efficiently and accurately identify the COVID-19 disease. METHODS: This study uses and fine-tunes seven convolutional neural networks including InceptionV3, ResNet50V2, Xception, DenseNet121, MobileNetV2, EfficientNet-B0, and EfficientNetV2 on COVID-19 detection. In addition, we proposes a lightweight convolutional neural network, LightEfficientNetV2, on small number of chest X-ray and CT images. Five-fold cross-validation was used to evaluate the performance of each model. To confirm the performance of the proposed model, LightEfficientNetV2 was carried out on three different datasets (NIH Chest X-rays, SARS-CoV-2 and COVID-CT). RESULTS: On chest X-ray image dataset, the highest accuracy 96.50% was from InceptionV3 before fine-tuning; and the highest accuracy 97.73% was from EfficientNetV2 after fine-tuning. The accuracy of the LightEfficientNetV2 model proposed in this study is 98.33% on chest X-ray image. On CT images, the best transfer learning model before fine-tuning is MobileNetV2, with an accuracy of 94.46%; the best transfer learning model after fine-tuning is Xception, with an accuracy of 96.78%. The accuracy of the LightEfficientNetV2 model proposed in this study is 97.48% on CT image. CONCLUSIONS: Compared with the SOTA, LightEfficientNetV2 proposed in this study demonstrates promising performance on chest X-ray images, CT images and three different datasets.
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spelling pubmed-90908612022-05-11 A lightweight CNN-based network on COVID-19 detection using X-ray and CT images Huang, Mei-Ling Liao, Yu-Chieh Comput Biol Med Article BACKGROUND AND OBJECTIVES: The traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether it is COVID-19 disease, which is easy to cause identification mistakes. In this study, the technology of convolutional neural network is expected to be able to efficiently and accurately identify the COVID-19 disease. METHODS: This study uses and fine-tunes seven convolutional neural networks including InceptionV3, ResNet50V2, Xception, DenseNet121, MobileNetV2, EfficientNet-B0, and EfficientNetV2 on COVID-19 detection. In addition, we proposes a lightweight convolutional neural network, LightEfficientNetV2, on small number of chest X-ray and CT images. Five-fold cross-validation was used to evaluate the performance of each model. To confirm the performance of the proposed model, LightEfficientNetV2 was carried out on three different datasets (NIH Chest X-rays, SARS-CoV-2 and COVID-CT). RESULTS: On chest X-ray image dataset, the highest accuracy 96.50% was from InceptionV3 before fine-tuning; and the highest accuracy 97.73% was from EfficientNetV2 after fine-tuning. The accuracy of the LightEfficientNetV2 model proposed in this study is 98.33% on chest X-ray image. On CT images, the best transfer learning model before fine-tuning is MobileNetV2, with an accuracy of 94.46%; the best transfer learning model after fine-tuning is Xception, with an accuracy of 96.78%. The accuracy of the LightEfficientNetV2 model proposed in this study is 97.48% on CT image. CONCLUSIONS: Compared with the SOTA, LightEfficientNetV2 proposed in this study demonstrates promising performance on chest X-ray images, CT images and three different datasets. Published by Elsevier Ltd. 2022-07 2022-05-11 /pmc/articles/PMC9090861/ /pubmed/35576824 http://dx.doi.org/10.1016/j.compbiomed.2022.105604 Text en © 2022 Published by Elsevier Ltd. 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
Huang, Mei-Ling
Liao, Yu-Chieh
A lightweight CNN-based network on COVID-19 detection using X-ray and CT images
title A lightweight CNN-based network on COVID-19 detection using X-ray and CT images
title_full A lightweight CNN-based network on COVID-19 detection using X-ray and CT images
title_fullStr A lightweight CNN-based network on COVID-19 detection using X-ray and CT images
title_full_unstemmed A lightweight CNN-based network on COVID-19 detection using X-ray and CT images
title_short A lightweight CNN-based network on COVID-19 detection using X-ray and CT images
title_sort lightweight cnn-based network on covid-19 detection using x-ray and ct images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9090861/
https://www.ncbi.nlm.nih.gov/pubmed/35576824
http://dx.doi.org/10.1016/j.compbiomed.2022.105604
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