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ORIGINAL ARTICLE
Year : 2018  |  Volume : 4  |  Issue : 1  |  Page : 16-21

Prediction for pathological image with convolutional neural network


1 Institute of Biomedical Engineering, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
2 Faculty of Education, Yuxi Normal University, Yuxi, Yunnan Province, China

Correspondence Address:
Jianfeng He
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan Province
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/digm.digm_46_17

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Background and Objectives: The diagnosis of cancer is concerned, and the prediction of cell carcinoma is of great importance for the treatment. Materials and Methods: First, we obtain a series of slices of tumor cell pathology in clinical data, with being followed training sets and test sets gained by adding data model. Then, we design a convolutional neural network training and prediction model. After that, we optimize parameters for training and prediction model, combining experience. Results: In experiment, the accuracy of the model predicting for cell carcinoma is 87.38%. Conclusions: This study provides a reference that predicts the extent of cell carcinoma progression by using deep learning model.


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