Towards Automated Tuberculosis detection using Deep Learning

TL;DR

This paper presents a deep learning-based method for detecting tuberculosis in microscopy images, aiming to improve diagnosis accuracy and support India's TB control efforts.

Contribution

It introduces a novel deep neural network approach for TB detection that achieves high recall and precision on microscopy images.

Findings

Recall of 83.78% and precision of 67.55% for bacillus detection.

Method accurately locates suspected TB germs in microscopy images.

Potential to develop into a high-sensitivity TB diagnostic system.

Abstract

Tuberculosis(TB) in India is the world's largest TB epidemic. TB leads to 480,000 deaths every year. Between the years 2006 and 2014, Indian economy lost US$340 Billion due to TB. This combined with the emergence of drug resistant bacteria in India makes the problem worse. The government of India has hence come up with a new strategy which requires a high-sensitivity microscopy based TB diagnosis mechanism. We propose a new Deep Neural Network based drug sensitive TB detection methodology with recall and precision of 83.78% and 67.55% respectively for bacillus detection. This method takes a microscopy image with proper zoom level as input and returns location of suspected TB germs as output. The high accuracy of our method gives it the potential to evolve into a high sensitivity system to diagnose TB when trained at scale.