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

Syed Azeem Inam, Daniyal Iqbal, Hassan Hashim and Mansoor Ahmed Khuhro

International Journal of Data Mining, Modelling and Management, 2024, vol. 16, issue 1, 101-112

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.

Keywords: tuberculosis; image classification; deep convolutional neural network; DCNN; accuracy; F1 score. (search for similar items in EconPapers)
Date: 2024
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