Colon Polyps Detection from Colonoscopy Images Using Deep Learning

TL;DR

This paper explores deep learning-based object detection, specifically YOLOv5 variants, for early colon polyp detection in colonoscopy images, demonstrating high accuracy and potential for improving colorectal cancer screening.

Contribution

The study evaluates multiple YOLOv5 architectures for colon polyp detection, identifying YOLOv5l as the most effective variant with high precision on colonoscopy images.

Findings

YOLOv5l achieved a mAP of 85.1%.

YOLOv5l had an average IoU of 0.86.

Deep learning improves polyp detection accuracy.

Abstract

Colon polyps are precursors to colorectal cancer, a leading cause of cancer-related mortality worldwide. Early detection is critical for improving patient outcomes. This study investigates the application of deep learning-based object detection for early polyp identification using colonoscopy images. We utilize the Kvasir-SEG dataset, applying extensive data augmentation and splitting the data into training (80\%), validation (20\% of training), and testing (20\%) sets. Three variants of the YOLOv5 architecture (YOLOv5s, YOLOv5m, YOLOv5l) are evaluated. Experimental results show that YOLOv5l outperforms the other variants, achieving a mean average precision (mAP) of 85.1\%, with the highest average Intersection over Union (IoU) of 0.86. These findings demonstrate that YOLOv5l provides superior detection performance for colon polyp localization, offering a promising tool for enhancing colorectal cancer screening accuracy.