An empirical approach towards detection of tuberculosis using deep convolutional neural network
Syed Azeem Inam, Daniyal Iqbal, Hassan Hashim, Mansoor Ahmed Khuhro
Abstract
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.