Geodesic
Models and methods of deep learning in medical image recognition and classification tasks
News of the Kabardin-Balkar scientific center of RAS, Tome 27 (2025) no. 2, pp. 103-112.

Voir la notice de l'article provenant de la source Math-Net.Ru

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, © Пшенокова И. А., Киясов М. Р., 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 
@article{IZKAB_2025_27_2_a6,
     author = {I. A. Pshenokova and M. R. Kiyasov},
     title = {Models and methods of deep learning in medical image recognition and classification tasks},
     journal = {News of the Kabardin-Balkar scientific center of RAS},
     pages = {103--112},
     publisher = {mathdoc},
     volume = {27},
     number = {2},
     year = {2025},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IZKAB_2025_27_2_a6/}
}
TY  - JOUR
AU  - I. A. Pshenokova
AU  - M. R. Kiyasov
TI  - Models and methods of deep learning in medical image recognition and classification tasks
JO  - News of the Kabardin-Balkar scientific center of RAS
PY  - 2025
SP  - 103
EP  - 112
VL  - 27
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IZKAB_2025_27_2_a6/
LA  - ru
ID  - IZKAB_2025_27_2_a6
ER  - 
%0 Journal Article
%A I. A. Pshenokova
%A M. R. Kiyasov
%T Models and methods of deep learning in medical image recognition and classification tasks
%J News of the Kabardin-Balkar scientific center of RAS
%D 2025
%P 103-112
%V 27
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IZKAB_2025_27_2_a6/
%G ru
%F IZKAB_2025_27_2_a6
I. A. Pshenokova; M. R. Kiyasov. Models and methods of deep learning in medical image recognition and classification tasks. News of the Kabardin-Balkar scientific center of RAS, Tome 27 (2025) no. 2, pp. 103-112. http://geodesic.mathdoc.fr/item/IZKAB_2025_27_2_a6/

[1] C. M. Bishop, Pattern recognition and machine learning (Information Science and Statistics), Springer, New York, 2007

[2] Z. Li et al., “A survey of convolutional neural networks: analysis, applications, and prospects”, IEEE transactions on neural networks and learning systems, 33:12 (2021), 6999–7019 | DOI

[3] M. Byra et al, “Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion”, Medical physics, 46:2 (2019), 746–755 | DOI

[4] Y. Horiuchi et al., “Convolutional neural network for differentiating gastric cancer from gastritis using magnified endoscopy with narrow band imaging”, Digestive diseases and sciences, 65 (2020), 1355–1363 | DOI

[5] J. Wang et al., “Integral real-time locomotion mode recognition based on GA-CNN for lower limb exoskeleton”, Journal of Bionic Engineering, 19:5 (2022), 1359–1373 | DOI

[6] D. Bhandari, S. Paul, A. Narayan, “Deep neural networks for multimodal data fusion and affect recognition”, International Journal of Artificial Intelligence and Soft Computing, 7:2 (2020), 130–145 | DOI

[7] A. Srivastava, A. Singh, A. K. Tiwari, “An efficient hybrid approach for the prediction of epilepsy using CNN with LSTM”, International Journal of Artificial Intelligence and Soft Computing, 7:3 (2022), 179–193 | DOI

[8] H. A. Khan et al., “Brain tumor classification in MRI image using convolutional neural network”, Mathematical Biosciences and Engineering, 17:5 (2021), 6203–6216 | DOI

[9] E. H. Houssein et al., “Hybrid quantum-classical convolutional neural network model for COVID-19 prediction using chest X-ray images”, Journal of Computational Design and Engineering, 9:2 (2022), 343–363 | DOI

[10] A. S. Raskopina, V. V. Bozhenko, T. M. Tatarnikova, “Using deep learning in diagnosing pneumonia from X-ray images”, News of higher educational institutions. Instrument engineering, 67:4 (2024), 315–320 (In Russian) | DOI

[11] C. Wan et al., “Research on classification algorithm of cerebral small vessel disease based on convolutional neural network”, Journal of Intelligent Fuzzy Systems, 44:2 (2023), 3107–3114

[12] M. Ashtari-Majlan, A. Seifi, M. M. Dehshibi, “A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images”, IEEE Journal of Biomedical and Health Informatics, 26:8 (2022), 3918–3926 | DOI

[13] F. Ekong et al., “Bayesian depth-wise convolutional neural network design for brain tumor MRI classification”, Diagnostics, 12:7 (2022), 1657 | DOI

[14] I. D. Apostolopoulos, S. Aznaouridis, M. Tzani, “An attention-based deep convolutional neural network for brain tumor and disorder classification and grading in magnetic resonance imaging”, Information, 14:3 (2023), 174 | DOI

[15] Y. Shi et al., “Residual convolutional neural network-based stroke classification with electrical impedance tomography”, IEEE Transactions on Instrumentation and Measurement, 71 (2022), 1–11 | DOI

[16] E. Yu. Shchetinin, L. A. Sevastyanov, “On deep learning transfer methods in biomedical image classification problems”, Computer Science and Its Applications, 15:4 (2021), 59–64 (In Russian) | DOI

[17] N. A. Shchukina, “Neural network models in the problem of classification of medical images”, Modeling, Optimization and Information Technology, 9:4 (35) (2021) (In Russian) | DOI

[18] K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014 | DOI

[19] A. Khan et al., “A survey of the recent architectures of deep convolutional neural networks”, Artificial intelligence review, 53 (2020), 5455–5516

[20] Brain tumor MRI dataset, (accessed 10.03.2025) https://www.kaggle.com/datasets/