ABSTRACT
A systematic analysis of artificial intelligence-based computer-aided diagnostic (CAD) systems for analyzing chest X-rays for diagnosing pulmonary tuberculosis (TB) is carried out in this paper. Tuberculosis (TB) is a transmissible disease that is one of the top 10 causes of death globally. In TB-affected countries, there is a strong need to improve care and screening. The four major areas in the literature on CAD system analysis of chest radiographs are: (i) pre-processing techniques, (ii) image segmentation, (iii) feature extraction, and (iv) classification. A detailed survey of the development of various phases of CAD programs for the diagnosis of TB is presented in this paper. The development of CAD systems aids in the early detection of TB.
