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

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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 
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     title = {Models and methods of deep learning in medical image recognition and classification tasks},
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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/