Detection of Tuberculosis based on Deep Learning based methods

Abstract 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.