Breast Cancer Image Classification Based on Fusion Multi-Network Deep Convolution Features and Sparse Double Relation Regularization Method
Wang Yongjun, Huang Fanglin, Huang Shan, Jiang Feng, Lei Baiying*, Wang Tianfu*
(School of Biomedical Engineering,Shenzhen University,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging,Shenzhen 518060,Guangdong,China)
Abstract：Breast cancer is one of the leading causes of cancer death worldwide. Existing diagnostic methods are mainly dependent on the observation with histopathological images,which is laborious,time-consuming,and relies on the doctor's professional knowledge and experience,making the diagnosis efficiency unsatisfactory. In view of these problems,this paper aimed to improve the breast cancer diagnostic accuracy and reduce the workload of doctors by devising a deep learning framework based on histological image. Specifically,this paper developed a classification model based on multi-network feature fusion and sparse double-relation regularized learning. First,the breast cancer pathological images were preprocessed by sub-image clipping and color enhancement. Then,three deep convolutional neural networks (InceptionV3,ResNet-50,and VGG-16) typical of deep learning model were used to extract multi-network deep convolution features of breast cancer pathological images. Third,by using two relations (“sample-sample” relation and “feature-feature” relation) and lF regularization,we proposed a supervised double relation regularization learning method to reduce feature dimension. Support vector machines was used to distinguish breast cancer pathological images into four categories:normal,benign,carcinoma in situ,and invasive carcinoma. In the experiment,by using 400 breast cancer pathological images in the ICIAR 2018 public data set to verify the proposed method,93% classification accuracy was obtained. Results showed that multi-network deep convolution fusion features could effectively capture rich image information,and sparse dual-relation regularization learning could effectively reduce feature honor and reduce noise interference,which will effectively improve the classification performance of the model.
王永军, 黄芳琳, 黄珊, 姜峰, 雷柏英, 汪天富. 基于融合多网络深层卷积特征和稀疏双关系正则化方法的乳腺癌图像分类研究[J]. 中国生物医学工程学报, 2020, 39(5): 532-540.
Wang Yongjun, Huang Fanglin, Huang Shan, Jiang Feng, Lei Baiying, Wang Tianfu. Breast Cancer Image Classification Based on Fusion Multi-Network Deep Convolution Features and Sparse Double Relation Regularization Method. Chinese Journal of Biomedical Engineering, 2020, 39(5): 532-540.
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