基于深度学习的胃癌病理图像分类方法

Pathological Image Classification of Gastric Cancer Based on Depth Learning

针对深度卷积神经网络能够有效提取图像深层特征的能力,选择在图像分类工作中表现优异的GoogLeNet和AlexNet模型对胃癌病理图像进行诊断。针对医学病理图像的特点,对GoogLeNet模型进行了优化,在保证诊断准确率的前提下降低了计算成本。在此基础上,提出模型融合的思想,通过综合不同结构和不同深度的网络模型,来学习更多的图像特征,以获取更有效的胃癌病理信息。实验结果表明,相比原始模型,多种结构的融合模型在胃癌病理图像的诊断上取得了更好的效果。

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.