Automated detection of COVID-19 coronavirus infection based on analysis of chest X-ray images by deep learning methods

被引:1
作者
Shchetinin, Evgenii Yu [1 ]
Sevastyanov, Leonid A. [2 ]
机构
[1] Financial Univ Govt Russian Federat, Dept Math, Moscow, Russia
[2] Peoples Friendship Univ Russia, Dept Appl Informat & Probabil, Moscow, Russia
来源
VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE | 2022年 / 58期
关键词
COVID-19; chest X-rays; deep learning; convolutional neural networks;
D O I
10.17223/19988605/58/9
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Early detection of COVID-19 infected patients is essential to ensure adequate treatment and reduce the load on the healthcare systems. One of effective methods for detecting COVID-19 is deep learning models of chest X-ray images. They can detect the changes caused by COVID-19 even in asymptomatic patients, so they have great potential as auxiliary systems for diagnostics or screening tools. This paper proposed a methodology consisting of the stage of pre-processing of X-ray images, augmentation and classification using deep convolutional neural networksXception, InceptionResNetV2, MobileNetV2, DenseNet121, ResNet50 and VGG16, previously trained on thelmageNet dataset. Next, they fine-tuned and trained on prepared data set of chest X-rays images. The results of computer experiments showed that theVGG16 model with fine tuning of the parameters demonstrated the best performance in the classification of COVID-19 with accuracy 99,09%, recall=98,318%, precision=99,08% and f1_score=98,78. This signifies the performance of proposed fine-tuned deep learning models for COVID-19 detection on chest X-ray images.
引用
收藏
页码:97 / 105
页数:9
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