Multi-Granularity Breast Cancer Pathological Image Recognition Model Based on CAP-Net
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.
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