Automated detection of COVID-19 coronavirus infection based on analysis of chest X-ray images by deep learning methods | Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitelnaja tehnika i informatika – Tomsk State University Journal of Control and Computer Science. 2022. № 58. DOI: 10.17223/19988605/58/9

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

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 theImageNet 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.

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Keywords

COVID-19, chest X-rays, deep learning, convolutional neural networks

Authors

Name	Organization	E-mail
Shchetinin Evgenii Yu.	Financial University under the Government of Russian Federation	riviera-molto@mail.ru
Sevastyanov Leonid A.	Peoples Friendship University of Russia	sevast@sci.pfu.edu.ru

Всего: 2

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