Few-shot brain tumor classification: meta- vs metric-learning comparison
Akhmetzhanova S. Serek A. Kashayev R. Kozhamuratova A.
October 2025Institute of Advanced Engineering and Science
Bulletin of Electrical Engineering and Informatics
2025#14Issue 53913 - 3922 pp.
Medical imaging requires accurate brain tumor recognition because precise classification is essential for early diagnosis and effective treatment planning. A major challenge in medical applications is that deep learning models typically require extensive amounts of labeled data to perform well. To address this, this research evaluates three few-shot learning (FSL) approaches-prototypical networks, Siamese networks, and model-agnostic meta-learning (MAML)-for brain tumor classification using the Figshare brain tumor dataset. The results show that prototypical networks consistently outperform the other approaches, achieving 89.07% accuracy (95% CI: 88.12–89.96%), 88.73% precision, and 88.67% recall, making them the optimal solution for this task. Siamese networks achieve 83.73% accuracy (95% CI: 82.64–84.76%), while MAML demonstrates significantly reduced performance, with 43.70% accuracy (95% CI: 42.10–45.22%). This study demonstrates that FSL can be applied effectively for medical image classification, with prototypical networks achieving the best performance in brain tumor detection. The inclusion of confidence intervals further validates the robustness and reliability of the results. Future research will focus on improving feature representation and exploring hybrid approaches to better handle rare tumor classes, thereby enhancing the clinical applicability of FSL models.
Brain tumor analysis , Brain tumors , Deep learning in medicine , Few-shot learning , Medical imaging
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Department of Computer Engineering, Astana IT University, Astana, Kazakhstan
School of Information Technology and Engineering, Kazakh-British Technical University (KBTU), Almaty, Kazakhstan
Department of Information Systems, SDU University, Kaskelen, Kazakhstan
School of Digital Technologies, Narxoz University, Almaty, Kazakhstan
Department of Computer Engineering
School of Information Technology and Engineering
Department of Information Systems
School of Digital Technologies
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