Framework for Tuberculosis Detection

Tuberculosis (TB) continues to be a noteworthy worldwide public health problem and the detection of TB plays crucial factor in improving treatment outcome and control of TB transmission. This paper acrobatically explores the state-of-the art methods in TB imaging analysis, especially artificial intelligence (AI) approaches, particularly diverse learning. This paper displays an extensive investigation of the capability of imaging modalities, namely chest radiographs and classification algorithms. An overview of recent advances in TB diagnostics is provided through examination of the usefulness of imaging modalities such as chest radiographs and computed tomography (CT) scans with preprocessing and classification tools. These findings demonstrate the power of AI driven approaches capable of achieving a greater degree of diagnostic accuracy, ultimately partnering with clinical decision-making processes while reducing any false positive rates.