Abstract

This project develops a deep learning model utilizing the DINOv2 architecture to automatically classify abnormalities detected in video capsule endoscopy (VCE) frames. The focus is on ten specific abnormalities, assisting healthcare professionals in diagnosing gastrointestinal disorders efficiently.