Colon Polyps Detection from Colonoscopy Images Using Deep Learning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909741397049344 |
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| author | Amin, Md Al Paul, Bikash Kumar |
| author_facet | Amin, Md Al Paul, Bikash Kumar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13188 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Colon Polyps Detection from Colonoscopy Images Using Deep Learning Amin, Md Al Paul, Bikash Kumar Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Colon Polyps Detection from Colonoscopy Images Using Deep Learning |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.13188 |