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Обнаружение коронавирусной инфекции COVID-19 на основе анализа рентгеновских снимков грудной клетки методами глубокого обучения
Е.Ю. Щетинин 1

1 Финансовый Университет при Правительстве РФ,
111123, Россия, г. Москва, ул. Щербаковская, д.38

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DOI: 10.18287/2412-6179-CO-1077

Страницы: 963-970.

Аннотация:
Раннее выявление пациентов с коронавирусной инфекцией COVID-19 имеет важное значение для обеспечения их адекватного лечения и снижения нагрузки на систему здравоохранения. Эффективным методом обнаружения COVID-19 является компьютерный анализ рентгеновских снимков грудной клетки методами глубокого обучения. В работе предложена методология, состоящая из этапов стандартизации размеров рентгеновских снимков к (224, 224), их классификации с использованием глубоких сверточных нейронных сетей Xception, InceptionResNetV2, MobileNetV2, DenseNet121, ResNet50 и VGG16, предварительно обученных на наборе данных ImageNet, а затем настроенных на наборе рентгеновских снимков грудной клетки. Результаты компьютерных экспериментов показали, что модель VGG16 с тонкой настройкой параметров продемонстрировала максимальную эффективность в классификации COVID-19 с показателями точности (accuracy) 99,09 %, полнота (recall) 99,483 %, прецизионность (precision) 99,08 %.

Ключевые слова:
COVID-19, рентгеновские снимки грудной клетки, глубокое обучение, сверточные нейронные сети.

Цитирование:
Щетинин, Е.Ю. Обнаружение коронавирусной инфекции COVID-19 на основе анализа рентгеновских снимков грудной клетки методами глубокого обучения / Е.Ю. Щетинин // Компьютерная оптика. – 2022. – Т. 46, № 6. – С. 963-970. – DOI: 10.18287/2412-6179-CO-1077.

Citation:
Shchetinin E.Y. 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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