Disease Recognition Using X-Ray Plates Using Deep Learning

Chest X-rays are essential in medical imaging, serving as a reliable diagnostic tool for various thoracic diseases. However, despite their critical role in healthcare, a vast amount of imaging data remains underutilized within Picture Archiving and Communication Systems (PACS) in hospitals and medical institutions. These stored images, along with their associated diagnoses, hold immense potential for training deep learning models, which require large datasets to enhance automated disease detection. This project aims to bridge that gap by utilizing the Chest Xray 8 dataset, a large-scale collection of labelled chest X-ray images covering multiple diseases, including pneumonia, tuberculosis, and COVID-19. By integrating this dataset with a deep learning models, Custom CNN, VGG19, ResNet50, DenseNet121, MobileNet, we aim to develop an AI-driven diagnostic model capable of identifying and classifying chest diseases with good accuracy. This approach can significantly enhance early detection, assist radiologists in decision-making, and improve healthcare accessibility, especially in regions with limited medical expertise. The project represents a step toward harnessing AI's power to optimize medical imaging, automate diagnostics, and revolutionize disease detection in clinical settings