测试技术学报2025，Vol.39Issue(1)：72-80,95,10.DOI:10.62756/csjs.1671-7449.2025011
基于CAP-Net的多粒度乳腺癌病理图像识别模型
Multi-Granularity Breast Cancer Pathological Image Recognition Model Based on CAP-Net
摘要
Abstract
In the field of medical image recognition,the feature extraction of images is closely related to the magnification of the image,so most models of breast cancer image recognition will perform experi-ments at different magnifications.However,in practical applications,it is hoped that different magnifica-tions of image information can be comprehensively utilized to comprehensively evaluate disease features and improve patient treatment effectiveness.In response to the above issues and the challenges of tumor classification in medical images,a classification model based on convolutional neural networks(CNN)and context-aware attentional pooling(CAP)is proposed,focusing on tumor categories without relying on specific magnifications.Firstly,the convolutional features of the image are extracted through CNN,and then the four levels of feature context information(including pixel-level,small-region,large-region and image-level)are comprehensively considered by combining them with the CAP module for classification.Using DenseNet121,MobileNetV2 and Xception three CNN networks combined with CAP,experiments were carried out the on BreakHis dataset.Four data of the same category with different magnifications were combined to identify eight types of breast cancer images.The accuracy of the model reached 96.87%,verifying its effectiveness in medical image classification.关键词
上下文感知注意力池化/乳腺癌病理图像/图像识别/卷积神经网络/多粒度图像识别Key words
context-aware attentional pooling/breast cancer pathological images/image recognition/con-volutional neural network/multi-granularity image recognition分类
信息技术与安全科学引用本文复制引用
张丹蕾,白艳萍,程蓉,续婷..基于CAP-Net的多粒度乳腺癌病理图像识别模型[J].测试技术学报,2025,39(1):72-80,95,10.基金项目
国家自然科学基金资助项目(61774137) （61774137）
山西省基础研究计划资助项目(202103021224195,202103021224212,202103021223189,20210302123019) （202103021224195,202103021224212,202103021223189,20210302123019）
