An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection

TL;DR

This paper introduces an explainable hybrid AI framework that significantly improves tuberculosis and symptom detection accuracy on chest X-rays, aiding early diagnosis in resource-limited settings.

Contribution

The study presents a novel teacher-student AI framework combining supervised and self-supervised learning for enhanced disease and symptom detection.

Findings

Achieved 98.85% accuracy in disease classification

Attained 90.09% macro-F1 score in symptom detection

Model demonstrates explainability based on relevant anatomical features

Abstract

Tuberculosis remains a critical global health issue, particularly in resource-limited and remote areas. Early detection is vital for treatment, yet the lack of skilled radiologists underscores the need for artificial intelligence (AI)-driven screening tools. Developing reliable AI models is challenging due to the necessity for large, high-quality datasets, which are costly to obtain. To tackle this, we propose a teacher--student framework which enhances both disease and symptom detection on chest X-rays by integrating two supervised heads and a self-supervised head. Our model achieves an accuracy of 98.85% for distinguishing between COVID-19, tuberculosis, and normal cases, and a macro-F1 score of 90.09% for multilabel symptom detection, significantly outperforming baselines. The explainability assessments also show the model bases its predictions on relevant anatomical features, demonstrating promise for deployment in clinical screening and triage settings.