Empowering Tuberculosis Screening with Explainable Self-Supervised Deep Neural Networks

Tuberculosis remains a global health crisis, disproportionately affecting resource-limited populations and remote regions, with over 10 million new infections annually. Though curable, early detection is crucial. Chest X-rays are the primary screening tool, but their use requires skilled radiologists, often unavailable in underserved areas. This highlights the need for AI-powered systems to assist in rapid screening. However, training reliable AI models requires large-scale, high-quality data, which is costly and challenging to obtain. To address this, we introduce an explainable self-supervised learning network for tuberculosis screening, achieving 98.14% accuracy, with recall and precision rates of 95.72% and 99.44%, respectively.