Abstract
Deep learning methods have already enjoyed an unprecedented success in medical imaging problems. Similar success has been evidenced when it comes to the detection of COVID-19 from medical images, therefore deep learning approaches are considered good candidates for detecting this disease, in collaboration with radiologists and/or physicians. In this paper, we propose a new approach to detect COVID-19 via exploiting a conditional generative adversarial network to generate synthetic images for augmenting the limited amount of data available. Additionally, we propose two deep learning models following a lightweight architecture, commensurating with the overall amount of data available. Our experiments focused on both binary classification for COVID-19 vs Normal cases and multi-classification that includes a third class for bacterial pneumonia. Our models achieved a competitive performance compared to other studies in literature and also a ResNet8 model. Our binary model achieved 98.7% accuracy, 100% sensitivity and 98.3% specificity, while our three-class model achieved 98.3% accuracy, 99.3% sensitivity and 98.1% specificity. Moreover, via adopting a testing protocol proposed in literature, our models proved to be more robust and reliable in COVID-19 detection than a baseline ResNet8, making them good candidates for detecting COVID-19 from posteroanterior chest X-ray images.
Original language | English |
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Article number | 104181 |
Number of pages | 9 |
Journal | Computers in Biology and Medicine |
Volume | 130 |
Early online date | 22 Dec 2020 |
DOIs | |
Publication status | Published - 1 Mar 2021 |
Bibliographical note
Funding Information:The authors would like to thank the multiple teams that have contributed to the release of the datasets used in this paper. We would also like to thank the Data Lab, which provided an MSc AI scholarship to the first author, making this project possible.
Keywords
- Generative adversarial networks
- Deep neural network
- covid-19
- medical informatics
- COVID-19
- Deep neural networks
- Bacterial pneumonia
- Chest x-rays
- Medical informatics
- Models, Theoretical
- Lung/diagnostic imaging
- Humans
- Male
- Tomography, X-Ray Computed
- Deep Learning
- SARS-CoV-2
- COVID-19/diagnostic imaging
- Female