Abstract
Breast cancer has become an important factor affecting human health. Diagnosis based on pathological images is considered the gold standard in the clinic. In this paper, an automatic breast cancer detection method based on hybrid features is proposed for pathological images. To obtain better segmentation results under conditions of crowded and chromatin-sparse nuclei, a 3-output convolutional neural network (CNN) is employed to segment the nuclei. Due to the weak correlation between the hematoxylin (H) and eosin (E) channels, texture features are separately extracted for the two channels, which provides more representative results. From multiple perspectives, the morphological features, spatial structural features and texture features are extracted and fused. Using a support vector machine (SVM) classifier with improved generalization, the pathological image is classified as benign or malignant on the basis of the relief method for feature selection. For the University of California, Santa Barbara database (UCSB), the classification accuracy of the method is 96.7%, and the area under the curve (AUC) is 0.983. The experimental results show that the proposed method yields superior classification performance compared with existing techniques.
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09 April 2019
The article Breast cancer classification in pathological images based on hybrid features, written by Cuiru Yu, Houjin Chen, Yanfeng Li, Yahui Peng, Jupeng Li and Fan Yang, was originally published electronically on the publisher’s internet portal (SpringerLink) on March 16, 2019 with open access.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under no. 61872030 and no. 61571036.
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Yu, C., Chen, H., Li, Y. et al. Breast cancer classification in pathological images based on hybrid features. Multimed Tools Appl 78, 21325–21345 (2019). https://doi.org/10.1007/s11042-019-7468-9
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DOI: https://doi.org/10.1007/s11042-019-7468-9