{"id":2517,"date":"2025-06-23T11:22:03","date_gmt":"2025-06-23T10:22:03","guid":{"rendered":"http:\/\/newskbncran.ru\/?page_id=2517"},"modified":"2026-06-04T09:38:19","modified_gmt":"2026-06-04T08:38:19","slug":"27-2-7-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-2-7-en\/","title":{"rendered":"27.2.7 en"},"content":{"rendered":"\n

Models and methods of deep learning in medical image recognition and classification tasks<\/strong><\/h1>\n\n\n\n

I.A. Pshenokova, M.R. Kiyasov<\/strong><\/strong><\/strong><\/strong><\/p>\n\n\n\n

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Abstract<\/strong>.<\/em><\/strong> 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, 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.<\/p>\n\n\n\n

Keywords<\/em><\/strong>:<\/strong> image recognition methods, deep learning methods, convolutional neural networks, transfer learning methods<\/p>\n\n\n\n

For citation<\/strong>.<\/strong> Pshenokova I.A., Kiyasov M.R. Models and methods of deep learning in medical image recognition and classification tasks. News of the Kabardino-Balkarian Scientific Center of RAS.<\/em> <\/strong>2025. Vol. 27. No. 2. Pp. 103\u2013112. DOI: 10.35330\/1991-6639-2025-27-2-103-112<\/p>\n\n\n\n

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  17. \u0429\u0443\u043a\u0438\u043d\u0430 \u041d. \u0410.<\/em> \u041d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0435\u0432\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0432 \u0437\u0430\u0434\u0430\u0447\u0435 \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u043c\u0435\u0434\u0438\u0446\u0438\u043d\u0441\u043a\u0438\u0445 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439 \/\/ \u041c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435, \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f \u0438 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0435 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438. 2021. \u0422. 9. \u2116 4(35). DOI: 10.26102\/2310-6018\/2021.35.4.022. EDN: MXPABV Shchukina N.A. Neural network models in the problem of classification of medical images. Modelirovaniye, optimizatsiya i informatsionnyye tekhnologii<\/em> [Modeling, Optimization and Information Technology]. 2021. Vol. 9. No. 4(35). DOI: 10.26102\/2310-6018\/2021.35.4.022. EDN: MXPABV. (In Russian)<\/li>\n\n\n\n
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  20. \u041d\u0430\u0431\u043e\u0440 \u0434\u0430\u043d\u043d\u044b\u0445 \u041c\u0420\u0422 \u043e\u043f\u0443\u0445\u043e\u043b\u0438 \u0433\u043e\u043b\u043e\u0432\u043d\u043e\u0433\u043e \u043c\u043e\u0437\u0433\u0430 [\u042d\u043b\u0435\u043a\u0442\u0440\u043e\u043d\u043d\u044b\u0439 \u0440\u0435\u0441\u0443\u0440\u0441].  URL: https:\/\/www.kaggle.com\/datasets\/masoudnickparvar\/brain-tumor-mri-dataset Nabor dannykh MRT opukholi golovnogo mozga<\/em> [Brain tumor MRI dataset]. [Electronic resource]. URL: https:\/\/www.kaggle.com\/datasets\/masoudnickparvar\/brain-tumor-mri-dataset. (accessed 10.03.2025)<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the authors<\/strong><\/summary>\n
    \n

    Inna A. Pshenokova<\/strong>, Candidate of Physical and Mathematical Sciences, Head of the Laboratory of Intelligent Living Environments, Institute of Computer Science and Problems of Regional Management \u2013 branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;<\/p>\n\n\n\n

    360000, Russia, Nalchik, 37-a I. Armand street;<\/p>\n\n\n\n

    Associate Professor of the Department of Computer Technology and Information Security, Kabardino-Balkarian State University named after Kh.M. Berbekov;<\/p>\n\n\n\n

    360004, Russia, Nalchik, 173 Chernyshevsky street;<\/p>\n\n\n\n

    pshenokova_inna@mail.ru, ORCID: https:\/\/orcid.org\/0000-0003-3394-7682, SPIN-code: 3535-2963<\/p>\n\n\n\n

    Murat R. Kiyasov<\/strong>, 4th year Student in the Field of Informatics and Computer Science; Kabardino-Balkarian State University named after Kh.M. Berbekov;<\/p>\n\n\n\n

    360004, Russia, Nalchik, 173 Chernyshevsky street;myrat7450@mail.ru<\/p>\n<\/div>\n<\/details>\n","protected":false},"excerpt":{"rendered":"

    Models and methods of deep learning in medical image recognition and classification tasks I.A. Pshenokova, M.R. Kiyasov 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 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-2517","page","type-page","status-publish","hentry"],"yoast_head":"\n27.2.7 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-2-7-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"27.2.7 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Models and methods of deep learning in medical image recognition and classification tasks I.A. Pshenokova, M.R. Kiyasov 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. 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Pshenokova, M.R. Kiyasov 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. 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