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

Content Provider	Europe PMC
Author	Huang, Mei-Ling Liao, Yu-Chieh
Copyright Year	2022
Abstract	Background and objectivesThe 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.MethodsThis 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).ResultsOn 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.ConclusionsCompared with the SOTA, LightEfficientNetV2 proposed in this study demonstrates promising performance on chest X-ray images, CT images and three different datasets.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9090861&blobtype=pdf
ISSN	00104825
Journal	Computers in Biology and Medicine [Comput Biol Med]
Volume Number	146
DOI	10.1016/j.compbiomed.2022.105604
PubMed Central reference number	PMC9090861
PubMed reference number	35576824
e-ISSN	18790534
Language	English
Publisher	Published by Elsevier Ltd.
Publisher Date	2022-05-11
Access Restriction	Open
Rights License	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. © 2022 Published by Elsevier Ltd.
Subject Keyword	Computer tomography X-ray COVID-19 Transfer learning
Content Type	Text
Resource Type	Article
Subject	Health Informatics Computer Science Applications