用深度学习检测肠镜图像中的息肉，提升早期癌变筛查准确率。
Colon Polyps Detection from Colonoscopy Images Using Deep Learning
- 基于YOLOv5架构，通过数据增强提升模型对息肉的识别能力。
- YOLOv5l在测试集上达到85.1% mAP和0.86平均交并比。
- 适合医疗AI开发者及内镜筛查研究人员参考。
结直肠息肉是结直肠癌的前兆，后者是全球癌症致死的主要原因。早期发现对改善患者预后至关重要。本研究探讨基于深度学习的目标检测在结肠镜图像中早期息肉识别的应用。使用Kvasir-SEG数据集，通过大量数据增强，并将数据划分为训练集（80%）、验证集（训练集的20%）和测试集（20%）。评估了三种YOLOv5变体（YOLOv5s、YOLOv5m、YOLOv5l）。实验结果表明，YOLOv5l优于其他变体，在测试集上实现85.1%的平均精度均值（mAP），平均交并比（IoU）达0.86。结果表明，YOLOv5l在息肉定位方面表现优异，为提高结直肠癌筛查准确性提供了有力工具。
原文摘要 · Abstract (English)
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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