基于深度学习的胸部X光影像分析系统  

Chest X-ray image analysis system based on deep learning

作　　者：周进凡 张荣芬 马治楠 葛自立 刘宇红 Zhou Jinfan;Zhang Rongfen;Ma Zhinan;Ge Zili;Liu Yuhong(College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学大数据与信息工程学院,贵州贵阳550025

出　　处：《电子技术应用》2018年第11期29-32,共4页Application of Electronic Technique

基　　金：贵州省科技计划项目(黔科合平台人才[2016]5707)

摘　　要：提出一种应用嵌入式技术和深度学习技术实现对胸部X光影像分析的设计方案。采用NIVIDIA公司生产的Jetson TX2作为核心板,配备以太网模块、WiFi模块等功能模块搭建该分析系统的硬件平台。在GPU服务器上利用MobileNets卷积神经网络对标注的胸部X光影像数据集进行训练,将训练好的神经网络模型移植到Jetson TX2核心板,在嵌入式平台下完成对胸腔积液、浸润、肺气肿、气胸以及肺不张症状的检测。利用美国国立卫生研究院提供的胸部X光影像数据进行测试,通过实验证明,该方法在识别准确率上优于其他的检测方法,同时识别所需时间比其他方法短。This paper proposes a design scheme for chest X-ray images analysis by using embedded technology and deep learning technology.The hardware platform of the analysis system using NIVIDIA′s Jetson TX2 as the core board,equipped with Ethernet modules,WiFi modules and other functional modules.It uses the MobileNets convolutional neural network on GPU server to train the marked chest X-ray image dataset then transplants the trained model to the Jetson TX2 core board,detecting the symptoms of pleural effusion,infiltration,emphysema,pneumothorax and atelectasis on the embedded platform.The chest X-ray image data provided by the National Institutes of Health(NIH)were tested in the trained model.Experiments have shown that this method gets higher accuracy and requires less time than other methods.

关 键 词：嵌入式技术 深度学习技术 胸部X光影像分析 Jetson TX2 MobileNets 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]