CoVScreen: Pitfalls and recommendations for screening COVID-19 using Chest X-rays

TL;DR

This paper introduces CoVScreen, a CNN-based method for COVID-19 screening using chest X-rays, addressing dataset limitations and demonstrating effective classification performance.

Contribution

The study curates a large-scale COVID-19 chest X-ray dataset, proposes a pre-processing pipeline, and introduces a novel CNN architecture for improved screening accuracy.

Findings

Effective COVID-19 screening demonstrated by CNN on curated dataset

Addressed data quality and imbalance issues in COVID-19 X-ray datasets

Proposed methodology outperforms existing approaches in evaluation metrics

Abstract

The novel coronavirus (COVID-19), a highly infectious respiratory disease caused by the SARS-CoV-2 has emerged as an unprecedented healthcare crisis. The pandemic had a devastating impact on the health, well-being, and economy of the global population. Early screening and diagnosis of symptomatic patients plays crucial role in isolation of patient to help stop community transmission as well as providing early treatment helping in reducing the mortality rate. Although, the RT-PCR test is the gold standard for COVID-19 testing, it is a manual, laborious, time consuming, uncomfortable, and invasive process. Due to its accessibility, availability, lower-cost, ease of sanitisation, and portable setup, chest X-Ray imaging can serve as an effective screening and diagnostic tool. In this study, we first highlight limitations of existing datasets and studies in terms of data quality, data imbalance, and evaluation strategy. Second, we curated a large-scale COVID-19 chest X-ray dataset from many publicly available COVID-19 imaging databases and proposed a pre-processing pipeline to improve quality of the dataset. We proposed CoVScreen, an CNN architecture to train and test the curated dataset. The experimental results applying different classification scenarios on the curated dataset in terms of various evaluation metrics demonstrate the effectiveness of proposed methodology in the screening of COVID-19 infection.