基于深度学习的胃癌病理图像分类方法

Abstract

Abstract: Due to that CNN can effectively extract deep features of the image,this paper used GoogLeNet and AlexNet models which have excellent performance in image classification to diagnose the pathological image of gastric cancer.Firstly,according to the characteristics of medical pathological images,this paper optimized the GoogLeNet model to reduce the computational cost under the premise of ensuring the accuracy of diagnosis.On this basis,it proposed the idea of model fusion.By combining more images with different structures and different depths,more effective pathological information of gastric cancer can be acquired.The experimental results show that the fusion model with multiple structures has achieved better results than the original model in the diagnosis of pathological images for gastric cancer.

Key words: CNN, Deep learning, Gastric cancer pathology, GoogLeNet optimization, Model fusion

CLC Number:

TP391.7

ZHANG Ze-zhong, GAO Jing-yang, LV Gang, ZHAO Di. Pathological Image Classification of Gastric Cancer Based on Depth Learning[J].Computer Science, 2018, 45(11A): 263-268.

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Pathological Image Classification of Gastric Cancer Based on Depth Learning

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