Tackling Tuberculosis: A Comparative Dive into Machine Learning for Tuberculosis Detection

TL;DR

This paper compares the performance of pretrained ResNet-50 and SqueezeNet models in diagnosing tuberculosis from chest X-ray images, highlighting the potential of machine learning for early detection especially in resource-limited settings.

Contribution

It introduces a comparative analysis of two deep learning models for TB detection using a Kaggle dataset, demonstrating the effectiveness of SqueezeNet over ResNet-50.

Findings

SqueezeNet achieved 89% accuracy and 98% precision.

ResNet-50 achieved 73% accuracy and 88% precision.

Both models show promise for TB diagnosis in resource-limited areas.

Abstract

This study explores the application of machine learning models, specifically a pretrained ResNet-50 model and a general SqueezeNet model, in diagnosing tuberculosis (TB) using chest X-ray images. TB, a persistent infectious disease affecting humanity for millennia, poses challenges in diagnosis, especially in resource-limited settings. Traditional methods, such as sputum smear microscopy and culture, are inefficient, prompting the exploration of advanced technologies like deep learning and computer vision. The study utilized a dataset from Kaggle, consisting of 4,200 chest X-rays, to develop and compare the performance of the two machine learning models. Preprocessing involved data splitting, augmentation, and resizing to enhance training efficiency. Evaluation metrics, including accuracy, precision, recall, and confusion matrix, were employed to assess model performance. Results showcase that the SqueezeNet achieved a loss of 32%, accuracy of 89%, precision of 98%, recall of 80%, and an F1 score of 87%. In contrast, the ResNet-50 model exhibited a loss of 54%, accuracy of 73%, precision of 88%, recall of 52%, and an F1 score of 65%. This study emphasizes the potential of machine learning in TB detection and possible implications for early identification and treatment initiation. The possibility of integrating such models into mobile devices expands their utility in areas lacking TB detection resources. However, despite promising results, the need for continued development of faster, smaller, and more accurate TB detection models remains crucial in contributing to the global efforts in combating TB.