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Related Experiment Video

Updated: Jan 12, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance.

Lin Qiu¹, Jian Ding¹, Chun-Xiao Lai²

¹Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China.

World Journal of Gastroenterology

|November 3, 2025

Summary

A new computer-aided diagnosis (CAD) system effectively detects and classifies colorectal polyps using the Yamada classification. This AI tool surpasses nonexpert endoscopists, improving polyp detection and classification in healthcare settings.

Keywords:

Artificial intelligence Computer-aided diagnosis Deep learning Endoscopy Yamada classification

Related Experiment Videos

Last Updated: Jan 12, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Colorectal cancer (CRC) detection relies on colonoscopy.
Computer-aided diagnosis (CAD) can enhance polyp identification and classification.
Accurate polyp classification is crucial for CRC management.

Purpose of the Study:

To develop a CAD system for polyp detection and Yamada classification.
To evaluate the CAD system's performance against endoscopist benchmarks.

Main Methods:

A YOLOv7 neural network model was trained on 24,045 polyp and 72,367 non-polyp images.
The system performed both polyp detection and Yamada classification.
Performance was assessed using frame-based and image-based evaluation metrics.

Main Results:

CAD achieved 96.2% F1-score for polyp detection, outperforming endoscopists.
For Yamada classification, CAD achieved an 80.2% F1-score, also outperforming endoscopists.
Image-based evaluation showed high accuracy (99.2% for detection, 97.2% for classification).

Conclusions:

A novel deep neural network-based CAD system was developed for polyp detection and Yamada classification.
The CAD system demonstrated superior performance compared to nonexpert endoscopists.
This CAD system has the potential to improve polyp detection and classification in community hospitals.