Computer Aided Tuberculosis Detection, A Review


Pragya Shukla
Dept. of Electronics and Communication Dr. B.R. Ambedkar National Institute of Technology Jalandhar
Jasleen Saini
Dept of CSE UIET, Panjab University Chandigarh, India
B.S. Saini
Department of ECE Dr. B.R. Ambedkar National Institute of Technology Jalandhar, India


This paper aims at presenting a complete picture of advances till now in the field of computer-aided detection of Pulmonary Tuberculosis using Chest X-ray Images. Advances are analyzed in chronological order as they happen and are divided into three phases in which technology shifted into new paradigms. Study concludes that although techniques that use Machine learning based methods for segmentation and classification are prevailing for the moment in terms of flexibility for very particular feature extraction in borderline cases where probabilistic methods can be tweaked according to requirements and accuracy, Deep Convolutional Neural Network based technique will secure higher standings as the computational capability and dataset management improves. Finally, briefly an attempt at using visualization techniques for borderline cases is discussed.

September 22, 2021
Online ISSN