Real-Time Polyp Detection From Endoscopic Images Using YOLOv8 With YOLO-Score Metrics for Enhanced Suitability Assessment

Abstract

Colorectal cancer has become a significant global health challenge and causes millions of fatalities every year. A key factor contributing to its high fatality rate is the absence of timely screening and accurate diagnosis. Early diagnosis is based on the detection and analysis of polyps which are cancer precursors linked to aging and declining health. Artificial intelligence-based computer-aided polyp detection (CAPD) tools are vital for affordable and efficient screening while mitigating the global impact of colorectal cancer. Aimed at improving the polyp detection capabilities, this research presents a real-time framework for detecting polyps in endoscopy frames, employing contrast-limited adaptive histogram equalization (CLAHE) and the proposed polyp detector YOLOv8p. The proposed detector uses multiple convolutional, C2f (Faster Cross Stage Partial Bottleneck with 2 convolutions), and SPPF (Spatial Pyramid Pooling-Fast) blocks. Moreover, a generic suitability assessment metric named YOLO-Score is proposed for overall performance evaluation. The proposed framework demonstrates superior clinical relevance and comparable performance to existing SOTA methods by achieving a mAP50 score of 95.5% and an $F_{1}$ score of 92.6% on the Hyper-Kvasir-Seg dataset. The research underscores the essential role of CAPD tools in improving diagnostic precision and streamlining real-time clinical processes in colorectal cancer screening.

Keywords

• CAPD
• CLAHE
• polyp detection
• YOLOv8
• YOLO-Score
• deep learning