This work presents a dual - model deep learning framework for rare event detection in capsule endoscopy videos. An ensemble-based rare event detection model is combined with an anatomy-aware gastrointestinal region detection model to improve reliability. Predictions from both models are merged during inference to reduce false positives and improve detection of rare abnormalities.