Colon polyp detection based on multi-scale and multi-level feature fusion and lightweight convolutional neural network

结直肠息肉的早期诊断和治疗对于预防结直肠癌至关重要。本文提出一种用于结直肠息肉自动检测与辅助诊断的轻量化卷积神经网络，首先基于53层卷积层骨干网络，引入空间金字塔池化模块，实现具有不同感受野大小的特征提取。然后，采用特征金字塔网络对骨干网络中不同尺度的特征图进行交叉融合，利用空间注意力模块提高对息肉图像边界和细节的感知能力。再进一步通过位置模式注意力模块，在不同层级的特征图中自动挖掘关键特征并整合，以实现快速高效准确的结直肠息肉自动检测。本文基于临床数据集对所提模型进行评估，其精度达到0.998 2，召回率达到0.998 8，F1分数达到0.998 4，平均精度（mAP）在交并比（IOU）为0.5时达到0.995 3，帧率74 帧/s，参数量9.08 M。相较于现有主流方法，本文所提出方法具有轻量化、运行配置要求低、高检测速度、高精度等特点，可为结直肠癌的早期检测和诊断提供可行的技术方法和重要工具。.; Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.