An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection
This work addresses the need for reliable AI-driven screening tools in clinical settings, particularly for tuberculosis and symptom detection, though it appears incremental in method.
The paper tackled the problem of early tuberculosis detection in resource-limited areas by proposing a hybrid AI framework for chest X-ray analysis, achieving 98.85% accuracy for disease classification and 90.09% macro-F1 score for symptom detection.
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.