TY  - EJOU
AU  - Yang, Junhao  
AU  - Chen, Chunxiao  
AU  - Zang, Qingyang  
AU  - Li, Jianfei  

TI  - Image Recognition of Breast Tumor Proliferation Level Based on Convolution Neural Network
T2  - Molecular \& Cellular Biomechanics

PY  - 2018
VL  - 15
IS  - 4
SN  - 1556-5300

AB  - 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.
KW  - Breast tumor
KW  -  proliferation level
KW  -  convolution neural network
KW  -  immunohistochemical staining
KW  -  pathological slide

DO  - 10.32604/mcb.2018.03824