Towards Automated Tuberculosis detection using Deep Learning

Tuberculosis(TB) in India is the world's largest TB epidemic [1] leading to 480,000 deaths every year [2]. Between the years 2006 and 2014, Indian economy lost $340 billion(USD) due to TB. This, combined with the emergence of drug resistant bacteria in India makes the problem worse [3]. The government of India has hence come up with a new strategy which requires a high-sensitivity microscopy based TB diagnosis mechanism [2]. We propose a new deep neural network based TB diagnosis methodology with recall and precision of 83.78% and 67.55% respectively for bacillus detection from microscopy images of sputum. The proposed method takes a microscopy image of sputum with proper zoom level as input and returns locations of suspected Mycobacterium tuberculosis bacilli as output. The high sensitivity of our method gives it the potential to evolve into an effective and accessible screening tool for TB detection, when trained at scale.