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Deep learning driven colorectal polyp analysis: a review of detection, classification and segmentation methods.

Divya S1, Sudha M1

  • 1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India.

Frontiers in Artificial Intelligence
|June 19, 2026
PubMed
Summary

Artificial intelligence (AI) significantly improves colorectal polyp detection, classification, and segmentation during colonoscopy. This review analyzes AI methods, datasets, and models to bridge the gap between research and clinical use for better colorectal cancer prevention.

Keywords:
colorectal polyp analysiscomputer aided diagnosisdeep learningmedical image analysispolyp classificationpolyp detectionpolyp segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Colorectal polyps are precursors to colorectal cancer, posing detection challenges during colonoscopy due to variations in appearance.
  • Accurate polyp identification is crucial for effective colorectal cancer screening and prevention.

Purpose of the Study:

  • To systematically review artificial intelligence (AI)-based methods for colorectal polyp detection, classification, and segmentation.
  • To analyze current state-of-the-art AI models, datasets, and preprocessing techniques in this field.

Main Methods:

  • Comprehensive literature review of AI applications in colorectal polyp analysis.
  • Analysis of publicly available datasets, data augmentation strategies, and common challenges like low contrast and class imbalance.
  • Evaluation of AI model architectures, performance trends, and standard metrics for benchmarking.

Main Results:

  • AI, particularly deep learning, shows significant promise in automating polyp detection, classification, and segmentation.
  • Identified strengths and weaknesses of various AI models and highlighted the importance of robust datasets and preprocessing.
  • Assessed standard performance metrics for consistent evaluation of AI algorithms.

Conclusions:

  • AI-based approaches offer a structured reference for analyzing colorectal polyps, enhancing diagnostic accuracy.
  • Discussed existing research gaps and future directions to facilitate the clinical translation of AI tools.
  • Emphasized the need to bridge the gap between experimental AI performance and real-world clinical deployment.