Lung Disease Detection Using Machine Learning Algorithms

Lung disease including pneumonia, tuberculosis, COPD, and COVID-19remain major public health challenges. Early, accurate diagnosis from chest X-rays or CT scans is crucial, yet manual interpretation is time-consuming and susceptible to human error. This study presents a comparative deep learning framework leveraging three architecturesVGG16, ResNet18, and Vision Transformer (ViT) to detect and classify lung disease from medical imaging. We curated a dataset of 3,475 chest X-ray images labelled into three classes: normal, lung opacity, and pneumonia. Data preprocessing included resizing, normalization, lung-region segmentation, and augmentation techniques such as histogram equalization and flipping. The dataset was split into 70% training, 15% validation, and 15% testing sets.