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Endoscopic colorectal polyp detection based on improved YOLOv8.

Jincai Huang, Jianyuan Zeng, Jinfeng Peng

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|March 5, 2025

Summary

This summary is machine-generated.

Artificial intelligence, using an improved YOLOv8 model, significantly enhances colorectal polyp detection accuracy during colonoscopies. This advanced computer detection technology aims to overcome the limitations of manual polyp identification.

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Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Colorectal tumors often originate from polyps, necessitating accurate detection during colonoscopy.
Manual polyp detection rates are limited (approx. 25%), impacted by subjective factors.
Improved polyp detection is critical for early diagnosis and treatment of colorectal cancer.

Purpose of the Study:

To apply artificial intelligence (AI) and deep learning for enhanced polyp detection in colorectal cancer screening.
To evaluate an improved YOLOv8 model for superior polyp identification accuracy and efficiency.

Main Methods:

Utilized an improved YOLOv8 deep learning model for automated polyp detection.
Compared the AI model's performance against existing artificial intelligence methods for polyp identification.

Main Results:

The improved YOLOv8 model demonstrated higher accuracy in detecting colorectal polyps.
The proposed AI approach showed increased efficiency compared to other artificial intelligence techniques.

Conclusions:

AI, specifically the improved YOLOv8 model, offers a promising solution to increase polyp detection rates.
This technology can potentially improve the early diagnosis of colorectal cancer by overcoming manual detection limitations.