PRIVACY-PRESERVING BRAIN TUMOR CLASSIFICATION USING VIT, EIGENCAM AND FEDERATED LEARNING
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Brain tumor diagnosis from MRI remains a critical challenge due to high inter-class morphological similarity, limited annotated data, and stringent patient privacy requirements in multi-institutional environments. This paper presents a novel unified framework integrating Vision Transformer (ViT) classification, EigenCAM-based Explainable AI (XAI), and Federated Learning (FL) with FedAvg aggregation. The ViT achieves 95% validation accuracy through frozen-backbone transfer learning from ImageNet-1k. EigenCAM generates spatially-precise attention heatmaps enabling tumor region localization with quantitative size estimation. The FL framework across three virtual hospital nodes achieves 93% accuracy in compliance with GDPR and HIPAA. Comparative evaluation against twelve state-of-the-art methods demonstrates that the proposed framework uniquely addresses interpretability, privacy, and classification accuracy simultaneously.
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Sureshkumar, (2026). Privacy-Preserving Brain Tumor Classification using VIT, Eigencam and Federated Learning. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.273
Sureshkumar, . "Privacy-Preserving Brain Tumor Classification using VIT, Eigencam and Federated Learning." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.273.
Sureshkumar, . "Privacy-Preserving Brain Tumor Classification using VIT, Eigencam and Federated Learning." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.273.
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