. CNN-based bacilli detection in sputum samples for tuberculosis diagnosis. In International Symposium on Biomedical Imaging (ISBI 2019). 2019.
Abstract
Tuberculosis (TB) is one of the infectious diseases that causes more deaths in low and middle-income countries. A low-cost method to diagnose TB consists in analyzing sputum smear samples through microscope observation. Manual identification and counting of bacilli is a very time consuming task and the sensitivity of the diagnosis depends on the availability of skilled technicians. We propose a computer vision technique based on a convolutional neural network (CNN) to automatically segment and count bacilli in sputum samples and predict the infection level.
