Deep learning driven colorectal polyp analysis: a review of detection, classification and segmentation methods
Abstract
Colorectal polyps are key determinants of colorectal cancer. Their accurate detection during colonoscopy has been a technically challenging work due to differences in shape size imaging conditions and texture. Emerging advances in Artificial Intelligence predominantly in deep learning have been making significant changes in the automatic detection and classification of polyps. This review presents a systematic and in-depth analysis of artificial intelligence-based methods for colorectal polyp detection classification and segmentation. Publicly available datasets are extensively reviewed along with data pre-processing and augmentation techniques that highlights low contrast noise and class imbalance. The review also investigates about the present state-of-the-art models for all three tasks. It is based on architecture designs performance trends and relative strengths. A thorough assessment has been made for the standard performance metrics used in existing literature for fair and consistent benchmarking. Finally existing gaps and future research paths have been discussed with an objective to fill the performance-translation gaps between experimental performance and clinical deployment. This review gives a structured reference for AI-based colorectal polyp analysis.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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