Combining Deep Learning with Traditional Features for Classification and Segmentation of Pathological Images of Breast Cancer

The automatic classification of pathological images of breast cancer has important clinical value. In order to improve the accuracy and efficiency of cancer detection, we implement two classifications in this paper. (1) Train deep convolutional neural network (CNN) models based on AlexNet and GoogLe...

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Bibliographic Details
Main Authors: He, Simin, Ruan, Jun, Long, Yi, Wang, Jianlian, Wu, Chenchen, Ye, Guanglu, Zhou, Jingfan, Yue, Junqiu, Zhang, Yanggeling
Format: Conference Proceeding
Language:English
Subjects:
Breast cancer
CNN
Feature extraction
feature learning
Image segmentation
Pathology
Support vector machines
texture features
Training
transfer learning
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Summary:The automatic classification of pathological images of breast cancer has important clinical value. In order to improve the accuracy and efficiency of cancer detection, we implement two classifications in this paper. (1) Train deep convolutional neural network (CNN) models based on AlexNet and GoogLeNet network structures. (2) Take the idea of transfer learning to complete the training of classification models. We use CNN to extract image features, then select distinguished features to simplify feature set, and combine them with texture features of images. At last, a Support Vector Machine (SVM) is used for feature learning and classification.
ISSN:2473-3547
DOI:10.1109/ISCID.2018.00007