Реєстраційний номер
2122U007126, Матеріали видань та локальних репозитаріїв
Категорія
Стаття
Назва роботи
The Intelligent Diagnosis Method of Covid-19 Based on the Lenet-Vit Deep Neural Network
Автор
Fedorov Eugene
Utkina Tetiana
Leshchenko Marina
Nechyporenko Olga
Rudakov Kostiantyn
Федоров Євген Євгенович
Уткіна Тетяна Юріївна
Лещенко Марина Миколаївна
Нечипоренко Ольга Володимирівна
Рудаков Костянтин Сергійович
Fedorov Eugene
Utkina Tetiana
Leshchenko Marina
Nechyporenko Olga
Rudakov Kostiantyn
Fedorov Yevhen Yevhenovych
Utkina Tetyana Yuriivna
Leshchenko Maryna Mykolaivna
Nechyporenko Olha Volodymyrivna
Rudakov Kostyantyn Serhiiovych
Дата публікації
01-01-2022
Постачальник інформації
Черкаський державний технологічний університет
Першоджерело
https://er.chdtu.edu.ua/handle/ChSTU/5397
Видання
CEUR Workshop Proceedings
Опис
The method for intelligent diagnosis of COVID-19 based on the LeNet-ViT deep neural
network was proposed. The LeNet-ViT model was created, it has the following advantages:
the input image is not square, which expands the scope; the input image is pre-compressed and
the new size depends on the original image size, and it is empirically determined, which
increases the model training speed and the model identification accuracy; the number of pairs
“convolutional layer - downsampling layer” depends on the image’s size, and it is
automatically determined, which increases the model classification accuracy; the number of
layer planes is automatically determined, which speeds up the definition of the model structure;
the patch size depends on the image size, and it is empirically determined, which increases the
model identification accuracy; the number of encoder blocks is empirically determined, which
increases the model learning speed; the use of a convolutional neural network allows to
efficiently extract features, and the use of a visual transformer allows to effectively analyze
these features. The proposed method for intelligent diagnosis of COVID-19 can be used in
various intelligent computer systems for medical diagnostics.
Додано/оновлено в НРАТ
2026-04-27
