Medical Imaging Datasets for Multimodal Disease Detection and Diagnosis Research

This collection of medical image datasets is a valuable resource for anyone involved in medical imaging and disease research. It includes a variety of images from different medical fields, all designed to support research in diagnosis and treatment. The datasets cover chest CT-scans, lung radiography, brain MRI, retinal imaging, and gastrointestinal tract imaging. The chest CT-scan dataset includes 867 images of normal lungs and three types of lung cancer—adenocarcinoma, large cell carcinoma, and squamous cell carcinoma—providing essential data for understanding lung cancer. The lung radiography dataset offers 1,198 X-ray images that include normal lungs and conditions like COVID-19, lung opacity, and viral pneumonia, making it useful for comparing COVID-19 with other respiratory diseases. The brain MRI dataset contains 253 scans of both normal brains and those with tumors, ideal for studying brain tumor detection. The retinal imaging dataset features 2,757 images covering normal retinas and seven types of retinal conditions, such as diabetic retinopathy and glaucoma, offering a comprehensive resource for eye disease research. Lastly, the gastrointestinal tract dataset contains 2,000 images of normal and diseased colons, including conditions like esophagitis, polyps, and ulcerative colitis, important for studying gastrointestinal health. Each dataset is carefully curated and resampled to ensure accuracy and prevent data leakage, making them reliable tools for research and machine learning.