COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Zebin, Tahmina and Rezvy, Shahadate (2020) COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization. Applied Intelligence. pp. 1-12. ISSN 0924-669X

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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 datasets. 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
Additional Information: Zebin, T., Rezvy, S. COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization. Appl Intell (2020). https://doi.org/10.1007/s10489-020-01867-1
Uncontrolled Keywords: deep learning,covid-19,chest radiographs,activation maps,deep neural networks,transfer learning,artificial intelligence ,/dk/atira/pure/subjectarea/asjc/1700/1702
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 22 Sep 2020 00:07
Last Modified: 19 Oct 2020 23:55
URI: https://ueaeprints.uea.ac.uk/id/eprint/76972
DOI: 10.1007/s10489-020-01867-1

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