AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images

TL;DR

AC-CovidNet is a novel attention-guided contrastive CNN that effectively detects Covid-19 in chest X-ray images, especially with limited data, by focusing on infected regions and learning discriminative features.

Contribution

It introduces a new attention-guided contrastive CNN architecture for Covid-19 detection in CXR images, addressing data scarcity and improving feature discrimination.

Findings

Achieves promising detection performance with limited training data

Focuses on infected regions via attention mechanism

Outperforms existing methods on public datasets

Abstract

Covid-19 global pandemic continues to devastate health care systems across the world. At present, the Covid-19 testing is costly and time-consuming. Chest X-Ray (CXR) testing can be a fast, scalable, and non-invasive method. The existing methods suffer due to the limited CXR samples available from Covid-19. Thus, inspired by the limitations of the open-source work in this field, we propose attention guided contrastive CNN architecture (AC-CovidNet) for Covid-19 detection in CXR images. The proposed method learns the robust and discriminative features with the help of contrastive loss. Moreover, the proposed method gives more importance to the infected regions as guided by the attention mechanism. We compute the sensitivity of the proposed method over the publicly available Covid-19 dataset. It is observed that the proposed AC-CovidNet exhibits very promising performance as compared to the existing methods even with limited training data. It can tackle the bottleneck of CXR Covid-19 datasets being faced by the researchers. The code used in this paper is released publicly at \url{https://github.com/shivram1987/AC-CovidNet/}.