News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences, 2025 Volume 27, Issue 2, Pages 103–112
(Mi izkab939)
|
Computer science and information processes
Models and methods of deep learning in medical image recognition and classification tasks
I. A. Pshenokovaab,
M. R. Kiyasova a Kabardino-Balkarian State University named after Kh.M. Berbekov,
360004, Russia, Nalchik, 173 Chernyshevsky street
b Institute of Computer Science and Problems of Regional Management –
branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences,
360000, Russia, Nalchik, 37-a I. Armand street
Abstract:
The paper presents a study and analysis of deep learning models and methods in the
problems of recognition and classification of brain tumor images. To compare the effectiveness of the
most relevant and available models based on convolutional neural networks, the VGG19, Xception,
© Ïøåíîêîâà È. À., Êèÿñîâ Ì. Ð., 2025
INFORMATICS AND INFORMATION PROCESSES
104 News of the Kabardino-Balkarian Scientific Center of RAS Vol. 27 No. 2 2025
and ResNet152 models were selected. The Xception model showed the best results. The purpose of this
work is to optimize and train the selected model using various methods to improve the accuracy of
diagnosing human brain tumors. A strategy for improving this model using transfer learning and data
augmentation methods is proposed and implemented. The tests show that the improved model
demonstrates higher accuracy and resistance to various types of data distortions, which makes it more
effective for image recognition and classification tasks.
Keywords:
image recognition methods, deep learning methods, convolutional neural networks, transfer
learning methods
UDC:
004.853
MSC: 68T42 Received: 14.03.2025
Revised: 07.04.2025
Accepted: 09.04.2025
DOI:
10.35330/1991-6639-2025-27-2-103-112
© , 2025