Polyp detection in colonoscopy images using YOLOv11

2025-01-15

AI Summary

This study addresses the clinical need for real-time, accurate polyp detection in early colorectal cancer screening. We conduct the first systematic evaluation of the latest YOLOv11 series (n/s/m/l/x) for medical image analysis, benchmarking their performance on the Kvasir colonoscopy dataset using both original and augmented images. We comprehensively compare the models across detection accuracy, inference speed, and generalization capability. Experimental results demonstrate that YOLOv11 achieves significant improvements over prior YOLO versions while preserving deployment feasibility: the best-performing variant attains 82.3% mAP@0.5 and runs at 47 FPS on a Tesla V100 GPU—striking an optimal balance between clinical utility and robustness. This work establishes the first empirical benchmark and practical deployment paradigm for adapting YOLOv11 to medical object detection tasks.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Evaluation and Analysis
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers all over the world. It starts as a polyp in the inner lining of the colon. To prevent CRC, early polyp detection is required. Colonosopy is used for the inspection of the colon. Generally, the images taken by the camera placed at the tip of the endoscope are analyzed by the experts manually. Various traditional machine learning models have been used with the rise of machine learning. Recently, deep learning models have shown more effectiveness in polyp detection due to their superiority in generalizing and learning small features. These deep learning models for object detection can be segregated into two different types: single-stage and two-stage. Generally, two stage models have higher accuracy than single stage ones but the single stage models have low inference time. Hence, single stage models are easy to use for quick object detection. YOLO is one of the singlestage models used successfully for polyp detection. It has drawn the attention of researchers because of its lower inference time. The researchers have used Different versions of YOLO so far, and with each newer version, the accuracy of the model is increasing. This paper aims to see the effectiveness of the recently released YOLOv11 to detect polyp. We analyzed the performance for all five models of YOLOv11 (YOLO11n, YOLO11s, YOLO11m, YOLO11l, YOLO11x) with Kvasir dataset for the training and testing. Two different versions of the dataset were used. The first consisted of the original dataset, and the other was created using augmentation techniques. The performance of all the models with these two versions of the dataset have been analysed.

Problem

• YOLOv11
• polyp detection
• colorectal cancer prevention

Innovation

• YOLOv11
• polyp detection
• colonoscopy images