An empirical approach towards detection of tuberculosis using deep convolutional neural network

Tuberculosis remains among the top disease, causing death all over the globe and its timely detection is a major concern for medical practitioners, especially after the emergence of the SARS-CoV-2 pandemic. Even with the recent advances in the methods for medical image classification, it is still challenging to diagnose tuberculosis without considering the associated historical and biological factors. There has been a great contribution of unsupervised learning in the development of techniques for image classification and the present study has utilised a deep convolutional neural network for detecting tuberculosis. It proposes a network comprising 54 layers having 59 connections. After computations, our proposed deep convolutional neural network attained an accuracy of 99.79%, 99.46%, and 99.5% for the classes of healthy, sick, and tuberculosis (TB) respectively for a public dataset, achieving higher accuracy as compared to other pre-trained network models.