Automated Scene Classification in Endoscopy Videos Using Convolutional Neural Networks

Endoscopy serves as a vital diagnostic tool in medical imaging, particularly in the examination of the esophagus, stomach, and intestines. This paper introduces a two-stage system for the automated classification of scene categories (Colonoscopy, Gastroscopy, Extracorporal, Blur) within endoscopy videos. The initial stage employs the Clear-Blur model to determine frame blurriness. If non-blurred, the subsequent stage utilizes the Three-Scene model for frame classification. The class results are then verified by the label of the video. This integrated system achieves 97% average classification accuracy evaluated on 197 clinical endoscopy video clips. Additionally, the system incorporates a temporal label accumulation algorithm, demonstrating over 90% average classification accuracy after 50 +/- 15 seconds of endoscopy entry into the gastrointestinal tracts.