Towards Automated Tuberculosis detection using Deep Learning
This addresses the critical problem of high-sensitivity TB diagnosis for public health in India, where TB causes significant deaths and economic losses, though the method appears incremental as it builds on existing deep learning approaches.
The paper tackles automated tuberculosis detection in India by proposing a deep neural network method that achieves 83.78% recall and 67.55% precision for bacillus detection in microscopy images.
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.