This project focuses on the detection of various lung conditions, including Normal, Pneumonia-Bacterial, Pneumonia-Viral, COVID-19, Tuberculosis, and Emphysema using chest X-ray images. The dataset used for this task is the Chest X-Ray 6 Classes Dataset available on Kaggle, which contains labeled X-ray images for each of the six categories. The main objective of this project is to develop a machine learning model that can automatically classify the chest X-ray images into the correct category, facilitating faster and more accurate diagnosis of these respiratory conditions.

For this task, Convolutional Neural Networks (CNN), along with advanced architectures such as MobileNet and DenseNet, are used to extract meaningful features from the X-ray images. These algorithms are chosen for their ability to process image data effectively, reducing computational complexity while maintaining high accuracy. The model is trained and validated on the Kaggle dataset, and its performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. The proposed system aims to provide a robust solution for early detection and classification of lung diseases, contributing to the improvement of medical diagnostics.

Keywords: Chest X-ray, Pneumonia, COVID-19, Tuberculosis, Emphysema, CNN, MobileNet, DenseNet.