# Towards Automated Tuberculosis detection using Deep Learning

arXiv: https://arxiv.org/abs/1801.07080 · HTML: https://mlsanity.com/paper/1801.07080/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2018.

## Built on by (most cited first, 3 of 3)

- [DeepGB-TB: A Risk-Balanced Cross-Attention Gradient-Boosted Convolutional Network for Rapid, Interpretable Tuberculosis Screening](https://mlsanity.com/paper/2508.02741/) (2025)
- [Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images](https://mlsanity.com/paper/2310.06080/) (2023)
- [Enhanced Tuberculosis Bacilli Detection using Attention-Residual U-Net and Ensemble Classification](https://mlsanity.com/paper/2501.03539/) (2025)

## Abstract (arXiv)

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 US340 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.