Chang, Victor, Mcwann, Siddharth, Hall, Karl, Xu, Qianwen Ariel and Ganatra, Meghana Ashok (2024). Diagnosis of COVID-19 CT Scans Using Convolutional Neural Networks. SN Computer Science, 5 (5),
Abstract
Machine learning technology, particularly neural networks, provides useful tools for diagnosing diseases. This study focuses on how convolutional neural networks can be implemented to diagnose COVID-19 through the processing of x-ray images. This study demonstrates how the convolutional neural networks DenseNet201, ResNet152, VGG16, and InceptionV3 can aid healthcare providers in the diagnosis of COVID-19. The models returned accuracies of 98.73%, 97.23%, 91.25% and 98.38% respectively. The results from these experiments are compared to previous studies by evaluating F1-score, accuracy, precision and recall. Additionally, the important problems of hyperparameter tuning and data imbalance are explored and addressed. Areas for future research in this area are also suggested.
Publication DOI: | https://doi.org/10.1007/s42979-024-02878-2 |
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Divisions: | College of Business and Social Sciences > Aston Business School > Operations & Information Management College of Business and Social Sciences College of Business and Social Sciences > Aston Business School |
Funding Information: | This work is partly supported by VC Research (VCR 0000174) for Prof Chang. |
Additional Information: | Copyright © The Author(s), 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. |
Uncontrolled Keywords: | Artificial intelligence,COVID-19,Convolutional neural networks,Diagnosis,Healthcare systems,Machine learning,Artificial Intelligence,General Computer Science,Computer Networks and Communications,Computer Science Applications,Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design |
Publication ISSN: | 2661-8907 |
Last Modified: | 07 Oct 2024 07:58 |
Date Deposited: | 11 Jun 2024 16:37 |
Full Text Link: | |
Related URLs: |
https://link.sp ... 979-024-02878-2
(Publisher URL) |
PURE Output Type: | Article |
Published Date: | 2024-06 |
Published Online Date: | 2024-06-07 |
Accepted Date: | 2024-04-05 |
Authors: |
Chang, Victor
(
0000-0002-8012-5852)
Mcwann, Siddharth Hall, Karl Xu, Qianwen Ariel ( 0000-0003-0360-7193) Ganatra, Meghana Ashok |