Classification of Chest X-ray Images Using Deep Convolutional Neural Network

被引：0
|
作者：
Hao, Ting [1 ]
Lu, Tong [1 ]
Li, Xia [1 ]
机构：
[1] China Jiliang Univ, Coll Informat Engn, Hangzhou, Peoples R China
来源：
2021 IEEE INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, INTL CONF ON CLOUD AND BIG DATA COMPUTING, INTL CONF ON CYBER SCIENCE AND TECHNOLOGY CONGRESS DASC/PICOM/CBDCOM/CYBERSCITECH 2021 | 2021年
基金：
浙江省自然科学基金;
关键词：
Convolutional Neural Network; Image Classification; Pneumonia Diagnosis; Chest X-ray images;
D O I：
10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00080
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this study, we proposed an improved convolutional neural network (CNN) structure for the classification of normal images and pneumonia infection images in chest X-ray images. We choose Google's open source Inceptiont-Resnet-V2 network as the basic building block, connect it with the squeeze-and-excitation (SENet) module followed by a feature fusion layer. We use the opensource chest X-ray dataset on the kaggle platform to conduct experiments on the proposed framework. It is shown in the results that the proposed method can effectively improve the accuracy of chest X-ray image classification compared with the related CNN methods reported in the literature.
引用
收藏
页码：440 / 445
页数：6
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