卷积神经网络在肝包虫病CT图像诊断中的应用  

Application of convolutional neural network in CT image diagnosis of hepatic echinococcosis

作　　者：刘志华 李丰军[2] 严传波[2] Liu Zhihua;Li Fengjun;Yan Chuanbo(College of Public Health,Xinjiang Medical University,Urumqi 830011,China;College of Medical Engineering Technology,Xinjiang Medical University,Urumqi 830011,China)

机构地区：[1]新疆医科大学公共卫生学院,新疆乌鲁木齐830011 [2]新疆医科大学医学工程技术学院,新疆乌鲁木齐830011

出　　处：《电子技术应用》2019年第11期17-20,共4页Application of Electronic Technique

基　　金：国家自然科学基金项目(81560294)

摘　　要：探讨卷积神经网络(Convonlutional Neural Network,CNN)在肝包虫病CT图像诊断中的应用。选取两种类型的肝包虫病CT图像进行归一化、改进的中值滤波去噪和数据增强等预处理。以LeNet-5模型为基础提出改进的CNN模型CTLeNet,采用正则化策略减少过拟合问题,加入Dropout层减少参数个数,对二分类肝包虫图像进行分类实验,同时通过反卷积实现特征可视化,挖掘疾病潜在特征。结果表明,CTLeNet模型在分类任务中取得了较好的效果,有望通过深度学习方法对肝包虫病提供辅助诊断和决策支持。This paper investigates the application of convolutional neural network(CNN)in CT image diagnosis of hepatic hydatidosis.Two types of CT images of hepatic hydatid disease were selected for normalization,improved median filtering denoising and data enhancement.Based on LeNet-5 model,an improved CNN model CTLeNet is proposed.Regularization strategy is adopted to reduce overfitting problems,dropout layer is added to reduce the number of parameters,and classification experiments are conducted on the images of dichotomous liver hydatid.Meanwhile,feature visualization is realized through deconvolution to explore the potential features of diseases.The results showed that CTLeNet model achieved good results in the classification task,and it was expected to provide auxiliary diagnosis and decision support for liver hydatidosis through deep learning.

关 键 词：深度学习 卷积神经网络 肝包虫病 图像分类 计算机辅助诊断 

分 类 号：TN919.8[电子电信—通信与信息系统] TP751.1[电子电信—信息与通信工程]