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ARTICLE

Image Recognition of Breast Tumor Proliferation Level Based on Convolution Neural Network

Junhao Yang1, Chunxiao Chen1,*, Qingyang Zang1, Jianfei Li1
Department of Biomedical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, 211106, China.
* Corresponding Author: Chunxiao Chen. Email: .

Molecular & Cellular Biomechanics 2018, 15(4), 203-214. https://doi.org/10.32604/mcb.2018.03824

Abstract

Pathological slide is increasingly applied in the diagnosis of breast tumors despite the issues of large amount of data, slow viewing and high subjectivity. To overcome these problems, a micrograph recognition method based on convolutional neural network is proposed for pathological slide of breast tumor. Combined with multi-channel threshold and watershed segmentation, a sample database including single cell, adhesive cell and invalid cell was established. Then, the convolution neural network with six layers is constructed, which has ability to classify the stained breast tumor cells with accuracy of more than 90%, and evaluate the proliferation level with relative error of less than 5%. The experimental result indicates the effectiveness of this approach, and is useful for providing an objective basis for evaluating the malignancy of breast tumors.

Keywords

Breast tumor, proliferation level, convolution neural network, immunohistochemical staining, pathological slide.

Cite This Article

Yang, J., Chen, C., Zang, Q., Li, J. (2018). Image Recognition of Breast Tumor Proliferation Level Based on Convolution Neural Network. Molecular & Cellular Biomechanics, 15(4), 203–214.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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