mlsanityData to 14 Oct 2026

An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection

arXiv 2510.18819 ↗First posted 21 Oct 2025cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 21 Oct 2025.

Builds on

10 of 24 references.

References · 10

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  5. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  6. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  7. 2020Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualizationcited by 50
  8. 2022AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillationcited by 17
  9. 2021TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases from Chest X-ray Imagescited by 8
  10. 2024Empowering Tuberculosis Screening with Explainable Self-Supervised Deep Neural Networkscited by 1

Built on by

Not cited yet.