Colon Polyps Detection from Colonoscopy Images Using Deep Learning
This work addresses colon polyp detection for colorectal cancer screening, but it is incremental as it applies an existing method to a specific medical dataset.
This study tackled the problem of early colon polyp detection from colonoscopy images using deep learning, achieving a mean average precision of 85.1% and an Intersection over Union of 0.86 with the YOLOv5l model.
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.