Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System

TL;DR

This paper presents a deep learning system that classifies COVID-19 and 14 other lung diseases from chest X-ray images, emphasizing unbiased data selection to improve fairness and accuracy in a realistic multi-class scenario.

Contribution

It introduces a novel CXR image selection technique that reduces bias and dataset size while maintaining high classification accuracy across 15 lung disease categories.

Findings

Effective COVID-19 detection in a 15-class setting

Reduced dataset bias and improved fairness

Maintained high accuracy with fewer images

Abstract

As of June 2021, the World Health Organization (WHO) has reported 171.7 million confirmed cases including 3,698,621 deaths from COVID-19. Detecting COVID-19 and other lung diseases from Chest X-Ray (CXR) images can be very effective for emergency diagnosis and treatment as CXR is fast and cheap. The objective of this study is to develop a system capable of detecting COVID-19 along with 14 other lung diseases from CXRs in a fair and unbiased manner. The proposed system consists of a CXR image selection technique and a deep learning based model to classify 15 diseases including COVID-19. The proposed CXR selection technique aims to retain the maximum variation uniformly and eliminate poor quality CXRs with the goal of reducing the training dataset size without compromising classifier accuracy. More importantly, it reduces the often hidden bias and unfairness in decision making. The proposed solution exhibits a promising COVID-19 detection scheme in a more realistic situation than most existing studies as it deals with 15 lung diseases together. We hope the proposed method will have wider adoption in medical image classification and other related fields.