Work

Real-Time Polyp Detection in Colonoscopy Using YOLOv8: A Fast and Accurate Deep Learning Approach

Year: 2025

Type: article

Abstract: Polyps that may develop on the inner surfaces of the intestines or rectum are considered the primarily cause of colorectal cancer (CRC). To enhance survival rates, it is essential to focus on early detection, accurate prognosis, and timely treatment, typically involving surgical removal of polyps. The employment of advanced computer-aided diagnosis systems (CADx) that utilize appropriate machine learning techniques, particularly deep learning methods, aids physicians in achieving a highly relevant detection of abnormalities during internal examinations of the human body. In this context, this paper discusses a deep learning framework for automated polyp detection utilizing the you only look once (YOLO) model. This paper introduces a detection system based on the YOLOv8n model, designed for simplicity, effectiveness, cost-efficiency, and potential significant support for healthcare providers and patients in the realm of polyp detection. The results achieved are compared with those obtained using the YOLOv7 model and demonstrate enhanced performance.

Source: International Journal of Online and Biomedical Engineering (iJOE)

DOI: 10.3991/ijoe.v21i10.55797

Authors: Kenza Redjimi, Mohammed Redjimi, Asma Talaa

Institution: University of Skikda

Language: English

Topic: Colorectal Cancer Screening and Detection

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $312