Detection & Classification of Tuberculosis HIV-Positive Patients using Deep Learning

Tuberculosis [TB] recently struck several countries all through the entire globe. According to the World Health Organization [WHO], there have been a calculated 2.8 million TB fatalities in 2022, with an extra 0.3 million deaths mainly due to TB illness in HIV-positive patients. The majority of Deaths can’t be avoided unless the disease is diagnosed early. Conventional diagnostic procedures, such as blood and urine tests or sputum testing, not only are inconvenient, and they also take a lot of time to analyze and cannot compare different drug-resistant phases of TB. In this paper, authors look at how deep learning-based method might be a good alternative to decision forest medical image-categorization systems. These experiments are conducted on chest X Ray of both tuberculosis and normal patient and identify the X-Rays of normal and tuberculosis patient separately. This experiment objective is to create generalized model to overcome the problems of the existing model. In this paper authors are experimenting various models such as normal CNN and Transfer Learning Methods.