Lightweight Deep Learning Models for Detecting COVID-19 from Chest X-ray Images

EasyChair Preprint 4494, version 3

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-classication 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.

Keyphrases: COVID-19, Chest X-rays, Deep Neural Networks, Generative Adversarial Networks, Medical Informatics, bacterial pneumonia

@booklet{EasyChair:4494,
  author    = {Stefanos Karakanis and Georgios Leontidis},
  title     = {Lightweight Deep Learning Models for Detecting COVID-19 from Chest X-ray Images},
  howpublished = {EasyChair Preprint 4494},
  year      = {EasyChair, 2022}}