COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization
Zebin, Tahmina and Rezvy, Shahadate 
ORCID: https://orcid.org/0000-0002-2684-7117
(2021)
COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization.
Applied Intelligence, 51
(2)
.
pp. 1010-1021.
ISSN 0924-669X
[Article]
(doi:10.1007/s10489-020-01867-1)
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Abstract
Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets1,2. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and Pneumonia from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 90%, 94.3%, and 96.8% for the VGG16, ResNet50, and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a CycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we implemented a gradient class activation mapping technique to highlight the regions of the input image that are important for predictions. Additionally, these visualizations can be used to monitor the affected lung regions during disease progression and severity stages.
| Item Type: | Article |
|---|---|
| Keywords (uncontrolled): | Article, Artificial Intelligence Applications for COVID-19, Detection, Control, Prediction, and Diagnosis, Activation maps, COVID-19, Deep neural networks, Transfer learning |
| Research Areas: | A. > School of Science and Technology > Computer Science |
| Item ID: | 31819 |
| Notes on copyright: | © The Author(s)
Open Access 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 http://creativecommons.org/licenses/by/4.0/. |
| Useful Links: | |
| Depositing User: | Jisc Publications Router |
| Date Deposited: | 13 Jan 2021 08:44 |
| Last Modified: | 13 Sep 2021 08:28 |
| URI: | https://eprints.mdx.ac.uk/id/eprint/31819 |
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