Lightweight deep learning models for detecting COVID-19 from chest X-ray images

https://doi.org/10.1016/j.compbiomed.2020.104181Get rights and content

Highlights

  • Lightweight deep neural networks can accurately detect Covid-19, Bacterial Pneumonia and Normal cases from chest X-rays.

  • A Generative Adversarial Network has been developed that can provide good quality synthetic COVID-19 images.

  • Proposed models demonstrated robustness against adversarial inputs across binary and multi-class cases.

  • A layer of interpretability has been added to the models highlighting the areas that contributed to the detection decision.

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 best performing 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.

Keywords

Generative adversarial networks
Deep neural networks
COVID-19
Bacterial pneumonia
Medical informatics
Chest x-rays

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