Towards Holistic Analysis of Wireless Capsule Endoscopic Videos: A Taxonomy-Driven Machine Learning Framework for Clinically Comprehensive WCE Frame Level Analysis

Manual perusal of capsule endoscopic videos is laborious due to their length, redundancy, and poor quality frames, risking misdetection. Limited representation of diverse anomalies and subtle lesions led to less generalized approaches targeting only individual or few anomalous findings. To deploy automated analysis in clinical settings effectively, a holistic, end-to-end approach is crucial, distinguishing between frame-level analysis and disease-level diagnosis. This study proposes a holistic framework for frame-level analysis based on a taxonomy of findings, leveraging clinical consensus. Clinical knowledge implicit in the taxonomy can enhance Computer Aided Diagnostics (CAD) systems. Convolutional Neural Networks (CNNs) trained at appropriate granularity levels in the taxonomy show improved performance metrics. Validation on the Kvasir-capsule dataset demonstrates a 2-4% performance boost. Taxonomy-based inference scores effectively resolve mimicked anomalies without additional learning.