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Detection of COVID-19 coronavirus infection in chest X-ray images with deep learning methods
E.Yu. Shchetinin 1

1 Financial University under the Government of the Russian Federation,
11123, Moscow, Russian Federation, Shcherbakovskaya, 38

 PDF, 1046 kB

DOI: 10.18287/2412-6179-CO-1077

Pages: 963-970.

Full text of article: Russian language.

Abstract:
Early detection of patients with COVID-19 coronavirus infection is essential in ensuring an adequate treatment and reducing the burden on the health care system. An effective method of detecting COVID-19 is computer analysis of chest X-rays. The paper proposes a methodology that consists of stages of formatting X-ray images to the size (224, 224) size, their classification using deep convolutional neural networks, such as Xception, InceptionResnetV2, MobileNetV2, DenseNet121, ResNet50 and VGG16, which are pre-trained on the ImageNet dataset and then fine-tuned on a set of chest X-rays. The results of computer experiments showed that the VGG16 model with fine-tuning of parameters demonstrated the best performance in the COVID-19 classification with accuracy = 99.09 %, recall = 99.483 %, precision = 99.08 % and f1_score = 99.281 %.

Keywords:
COVID-19, chest X-rays, deep learning, finetuning, convolutional neural networks.

Citation:
Shchetinin EY. Detection of COVID-19 coronavirus infection in chest X-ray images with deep learning methods. Computer Optics 2022; 46(6): 963-970. DOI: 10.18287/2412-6179-CO-1077.

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