Enhancing Normal-Abnormal Classification Accuracy in Colonoscopy Videos via Temporal Consistency

This paper proposes a novel hierarchical approach to improve the accuracy of the classification of normal-vs-abnormal frames in white-light colonoscopy videos. The existing approaches label each frame independently, without considering the temporal consistency between adjacent frames. Temporal consistency, however, can improve the classification accuracy in the presence of unclear/uncertain images. We propose to leverage temporal consistency between adjacent frames for colonoscopy video frame classification using a novel hierarchical classifier. Comparative experiments with five challenging full colonoscopy videos show that the proposed approach considerably improves the mean class normal/abnormal classification accuracy compared to the approaches where the frames are classified independently.