Diagnosis of Tuberculosis using Deep Learning

Abstract

Preventing the spread of tuberculosis (TB) and enhancing patient outcomes are largely dependent on early detection. This presents a new method for detecting tuberculosis by utilizing convolutional neural networks to examine X-ray pictures of the chest. To extract and learn hierarchical information from x-ray images- a critical component for effective predictions the CNN’s architecture has been meticulously built. By examining the interpretability of the CNN model, the study sheds light on the characteristics and patterns that the network considers important when forming predictions. For medical professionals to trust the model and use it as a useful diagnostic tool, this feature is essential. Firstly, it outlines the limitations of conventional TB diagnostic methods, emphasizing the need for more accurate, efficient, and accessible solutions.