Empowering Tuberculosis Screening with Explainable Self-Supervised Deep Neural Networks

TL;DR

This paper presents an explainable self-supervised deep learning model for tuberculosis screening using chest x-rays, achieving high accuracy and supporting early detection in resource-limited settings.

Contribution

It introduces a novel explainable self-supervised learning network specifically designed for tuberculosis screening from chest x-ray images.

Findings

Achieved 98.14% overall accuracy in tuberculosis detection.

High recall of 95.72% and precision of 99.44% in identifying TB cases.

Effectively captures clinically significant features for screening.

Abstract

Tuberculosis persists as a global health crisis, especially in resource-limited populations and remote regions, with more than 10 million individuals newly infected annually. It stands as a stark symbol of inequity in public health. Tuberculosis impacts roughly a quarter of the global populace, with the majority of cases concentrated in eight countries, accounting for two-thirds of all tuberculosis infections. Although a severe ailment, tuberculosis is both curable and manageable. However, early detection and screening of at-risk populations are imperative. Chest x-ray stands as the predominant imaging technique utilized in tuberculosis screening efforts. However, x-ray screening necessitates skilled radiologists, a resource often scarce, particularly in remote regions with limited resources. Consequently, there is a pressing need for artificial intelligence (AI)-powered systems to support clinicians and healthcare providers in swift screening. However, training a reliable AI model necessitates large-scale high-quality data, which can be difficult and costly to acquire. Inspired by these challenges, in this work, we introduce an explainable self-supervised self-train learning network tailored for tuberculosis case screening. The network achieves an outstanding overall accuracy of 98.14% and demonstrates high recall and precision rates of 95.72% and 99.44%, respectively, in identifying tuberculosis cases, effectively capturing clinically significant features.