Colon Polyps Detection from Colonoscopy Images Using Deep Learning

Md Al Amin, Bikash Kumar Paul
8/14/2025
eess.IVcs.CV

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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.

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Cite this paper

@article{amin2025colon,
  title  = {Colon Polyps Detection from Colonoscopy Images Using Deep Learning},
  author = {Md Al Amin and Bikash Kumar Paul},
  year   = {2025},
  eprint = {2508.13188},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.13188v1}
}

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