Medical Image Analysis for Early Detection of Tuberculosis Using Deep Learning and Explainable Artificial Intelligence (XAI)

Abstract

Tuberculosis (TB) remains a significant global public health issue, particularly affecting resource-constrained countries like the Philippines. Early and accurate diagnosis of TB is crucial for effective patient management and control of its spread. However, conventional diagnostic processes relying on human interpretation of chest X-rays are prone to delays, variability, and errors. This study proposes an automated diagnostic solution using state-of-the-art convolutional neural network (CNN) architectures—ResNet50, EfficientNetB0, VGG19, and InceptionV3— to classify chest X-ray images as either TB-positive or TB-negative. Among the evaluated models, InceptionV3 achieved superior performance. The system integrated preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve image quality, enhancing prediction accuracy. Moreover, Gradient-weighted Class Activation Mapping (Grad-CAM) was implemented as an Explainable Artificial Intelligence (XAI) technique, significantly enhancing the interpretability of model predictions by visually indicating regions relevant to TB pathology. A user-friendly Django-based web application was developed, enabling healthcare professionals to interact seamlessly with the diagnostic system. Despite high performance, rare instances of false positives and false negatives were observed, emphasizing the necessity for clinical validation of AI-driven diagnostics. Overall, this research contributes towards improving TB diagnostic accuracy, reducing healthcare worker burden, and facilitating interpretability in clinical practice.