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.
Original language | English |
---|---|
Article number | 625 |
Number of pages | 17 |
Journal | SN Computer Science |
Volume | 5 |
Issue number | 5 |
Early online date | 7 Jun 2024 |
DOIs | |
Publication status | Published - Jun 2024 |
Bibliographical note
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 acopy of this licence, visit https://creativecommons.org/licenses/by/4.0/.
Keywords
- Artificial intelligence
- COVID-19
- Convolutional neural networks
- Diagnosis
- Healthcare systems
- Machine learning