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

Tuberculosis (TB) is caused by the bacterium Mycobacteriumtuberculosis, 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. Experiments with the model demonstrate classification accuracy results of 98.93% for ResNet-18, 98.60% for ResNet-50, and 33.33 % for VGG16. These findings indicate that the proposed method performs well on certain feature extraction algorithms in mitigating data imbalance. Ultimately the implementation of few-shot learning in handling data imbalance proves to be a promising solution for enhancing TB detection accuracy.