# Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images

arXiv: https://arxiv.org/abs/2409.11644 · HTML: https://mlsanity.com/paper/2409.11644/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization](https://mlsanity.com/paper/2007.14895/) (2020)
- [Towards Radiologist-Level Accurate Deep Learning System for Pulmonary Screening](https://mlsanity.com/paper/1807.03120/) (2018)

## Abstract (arXiv)

Tuberculosis (TB) is caused by the bacterium Mycobacterium tuberculosis, primarily affecting the lungs. Early detection is crucial for improving treatment effectiveness and reducing transmission risk. Artificial intelligence (AI), particularly through image classification of chest X-rays, can assist in TB detection. However, class imbalance in TB chest X-ray datasets presents a challenge for accurate classification. In this paper, we propose a few-shot learning (FSL) approach using the Prototypical Network algorithm to address this issue. We compare the performance of ResNet-18, ResNet-50, and VGG16 in feature extraction from the TBX11K Chest X-ray dataset. Experimental results demonstrate classification accuracies of 98.93% for ResNet-18, 98.60% for ResNet-50, and 33.33% for VGG16. These findings indicate that the proposed method outperforms others in mitigating data imbalance, which is particularly beneficial for disease classification applications.