Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning
Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning
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DOI：
10.1016/j.measurement.2020.108046
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发表时间：
2020-12-01
期刊：
影响因子：
5.6
通讯作者：
Hemanth, D. Jude
中科院分区：
文献类型：
--
作者：
Jain, Rachna;Nagrath, Preeti;Hemanth, D. Jude
全世界每年有大量儿童死于肺炎。据估计，5岁以下儿童中有120万例肺炎病例，其中88万人在2016年死亡。因此，肺炎是儿童死亡的一个主要原因，在南亚和撒哈拉以南非洲的发病率很高。即使在美国这样的发达国家，肺炎也是十大死亡原因之一。肺炎的早期发现和治疗可以大大降低高流行率国家儿童的死亡率。因此，本文提出了卷积神经网络模型，使用X射线图像检测肺炎。几个卷积神经网络被训练成将X射线图像分类成两类，即，肺炎和非肺炎，通过改变各种参数，超参数和卷积层的数量。本文提出了六种模型。第一和第二模型分别由两个和三个卷积层组成。其他四个模型是预先训练的模型，分别是VGG 16、VGG 19、ResNet 50和Inception-v3。第一和第二模型分别达到85.26%和92.31%的验证准确率。VGG 16、VGG 19、ResNet 50和Inception-v3的准确率分别为87.28%、88.46%、77.56%和70.99%。(C)2020爱思唯尔有限公司版权所有。
A large number of children die due to pneumonia every year worldwide. An estimated 1.2 million episodes of pneumonia were reported in children up to 5 years of age, of which 880,000 died in 2016. Hence, pneumonia is a major cause of death amongst children, with high prevalence rate in South Asia and Sub-Saharan Africa. Even in a developed country like the United States, pneumonia is among the top 10 causes of deaths. Early detection and treatment of pneumonia can reduce mortality rates among children significantly in countries having a high prevalence. Hence, this paper presents Convolutional Neural Network models to detect pneumonia using x-ray images. Several Convolutional Neural Networks were trained to classify x-ray images into two classes viz., pneumonia and non-pneumonia, by changing various parameters, hyperparameters and number of convolutional layers. Six models have been mentioned in the paper. First and second models consist of two and three convolutional layers, respectively. The other four models are pre-trained models, which are VGG16, VGG19, ResNet50, and Inception-v3. The first and second models achieve a validation accuracy of 85.26% and 92.31% respectively. The accuracy of VGG16, VGG19, ResNet50 and Inception-v3 are 87.28%, 88.46%, 77.56% and 70.99% respectively. (C) 2020 Elsevier Ltd. All rights reserved.