# Classification of Pneumonia and Tuberculosis from Chest X-rays

arXiv: https://arxiv.org/abs/2103.14562 · HTML: https://mlsanity.com/paper/2103.14562/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays](https://mlsanity.com/paper/1703.08770/) (2017)

## Abstract (arXiv)

Artificial intelligence (AI) and specifically machine learning is making inroads into number of fields. Machine learning is replacing and/or complementing humans in a certain type of domain to make systems perform tasks more efficiently and independently. Healthcare is a worthy domain to merge with AI and Machine learning to get things to work smoother and efficiently. The X-ray based detection and classification of diseases related to chest is much needed in this modern era due to the low number of quality radiologists. This thesis focuses on the classification of Pneumonia and Tuberculosis two major chest diseases from the chest X-rays. This system provides an opinion to the user whether one is having a disease or not, thereby helping doctors and medical staff to make a quick and informed decision about the presence of disease. As compared to previous work our model can detect two types of abnormality. Our model can detect whether X-ray is normal or having abnormality which can be pneumonia and tuberculosis 92.97% accurately.