A Study on Tuberculosis With Deep Learning and Machine Learning Approaches

Abstract

A great threat to global health continues to be posed by the extremely contagious illness of tuberculosis (TB). Controlling the spread of TB and enhancing patient outcomes depend on early and precise detection. By evaluating medical images and minimizing the time and effort needed for manual analysis, machine learning (ML) approaches have shown considerable promise in assisting in the diagnosis of tuberculosis (TB). In this study we cover the most recent ML-based TB detection techniques in and go over their benefits and drawbacks. Deep learning, conventional ML algorithms, and methods based on computer vision are among the techniques examined.