Detection of Tuberculosis based on Deep Learning based methods

作     者：Murali Krishna Puttagunta S. Ravi 

作者机构：Research Scholar Department of Computer Science School of Engineering and Technology Pondicherry University India Associate Professor Department of Computer Science School of Engineering and Technology Pondicherry University India 

出 版 物：《Journal of Physics: Conference Series》 

年 卷 期：2021年第1767卷第1期

学科分类：07[理学] 0702[理学-物理学] 

摘      要：Pulmonary Tuberculosis (TB) one of the transmissible diseases, which is one of the top ten causes of death worldwide. The need to strengthen the treatment and screening in TB affected countries. In this paper, a systematic review is carried on deep learning-based computer-aided diagnostic (CAD) systems that are used to analyze chest X-rays for diagnosing pulmonary tuberculosis (TB). Deep learning has recently become one of the most successful techniques, particularly in the analysis of medical images. In Deep learning Convolutional Neural Networks (CNNs) are widely used for TB detection. A CNN model is commonly comprised of convolutional layers, sub-sampling / pooling layers, and fully connected layers. This paper also presents a comprehensive survey on the CNN models for the detection of TB. The progression of computer-aided diagnostic (CAD) systems has sped up the early diagnosis of TB.