EARLY DETECTION OF TUBERCULOSIS WITH CHEST XRAY WITH DEEP LEARNING

Tuberculosis (TB) remains one of the most serious infectious diseases worldwide, causing millions of deaths annually, particularly in low-and middle-income countries. Early detection plays a crucial role in reducing transmission, improving treatment outcomes, and saving lives.This project focuses on developing and analyzing techniques for the early diagnosis of tuberculosis using a combination of clinical data, imaging techniques (such as chest X-rays), and machine learning models. By leveraging modern data analysis tools and artificial intelligence, the system aims to identify TB-positive cases at an early stage with high accuracy. The use of automated detection tools can assist healthcare professionals in remote or resource-limited settings, providing rapid screening and reducing diagnostic delays.This approach contributes to public health efforts in controlling TB and aligns with global.