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In medical image diagnostics, classifying brain tumors appropriately would result in better outputs on the treatment end. So, this paper suggests an innovative approach for brain tumor image classification through federated learning and transfer learning to ensure privacy of data within the medical institutes. This methodology applies a pre-trained model, VGG-16 with ImageNet, and transfers the learned model, using less of the highly labeled data. The federated framework that uses the FedAvg algorithm for model aggregation ensures to keep data decentralized and private, thus enabling collaborative building of models without sharing actual data. It conducted its experiment on a dataset spread over 10 clients of brain tumors and encompassing a variety of medical centers. It had four classes-glioma, meningioma, notumor, and pituitary. The model performs good classification performance with the aid of a macro-averaged F1-score of 0.97. It shows the possibility of real-world health care, such a high-accuracy, decentralized AI models paving the way for privacy-focused collaborative advances in medical diagnostics.

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Collaborative and Privacy-Enhanced Medical Image Diagnosis Using Transfer Federated Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Aviral Kumar Goyal,
  • Manish Pandey,
  • Dhirendra Pratap Singh,
  • Jaytrilok Choudhary,
  • Rahul Haripriya

摘要

In medical image diagnostics, classifying brain tumors appropriately would result in better outputs on the treatment end. So, this paper suggests an innovative approach for brain tumor image classification through federated learning and transfer learning to ensure privacy of data within the medical institutes. This methodology applies a pre-trained model, VGG-16 with ImageNet, and transfers the learned model, using less of the highly labeled data. The federated framework that uses the FedAvg algorithm for model aggregation ensures to keep data decentralized and private, thus enabling collaborative building of models without sharing actual data. It conducted its experiment on a dataset spread over 10 clients of brain tumors and encompassing a variety of medical centers. It had four classes-glioma, meningioma, notumor, and pituitary. The model performs good classification performance with the aid of a macro-averaged F1-score of 0.97. It shows the possibility of real-world health care, such a high-accuracy, decentralized AI models paving the way for privacy-focused collaborative advances in medical diagnostics.