Deep Learning and Automated Diagnostic Systems for Tuberculosis Detection: A Comprehensive Study

Abstract

This study provides a comprehensive overview of tuberculosis (TB) as a major global public health concern and highlights the evolving role of advanced computational technologies in its diagnosis and management. Despite significant improvements in India’s healthcare system, communicable diseases such as TB continue to pose serious challenges, particularly in low- and middle-income countries. The study emphasizes the epidemiology, transmission, symptoms and global burden of TB, with a special focus on India, which accounts for a substantial share of global cases. It further explores the limitations of traditional diagnostic methods and the growing importance of computerized and automated diagnostic systems. The integration of deep learning, machine learning and hybrid approaches such as genetic algorithms and neuro-fuzzy systems has significantly enhanced diagnostic accuracy, efficiency and early detection capabilities. These technologies enable faster processing of medical images, reduce human error and support clinical decision-making, especially in resource-constrained settings. The study concludes that the adoption of advanced artificial intelligence-based diagnostic tools, along with strong public health strategies, can play a crucial role in achieving the goal of tuberculosis elimination.