IPC-CNN: A Robust Solution for Precise Brain Tumor Segmentation Using Improved Privacy-Preserving Collaborative Convolutional Neural Network

• Abdul Raheem,
• Zhen Yang,
• Haiyang Yu,
• Muhammad Yaqub,
• Fahad Sabah,
• Shahzad Ahmed,
• Malik Abdul Manan,
• Imran Shabir Chuhan,

Vol. 18, No. 9, September 30, 2024
10.3837/tiis.2024.09.007, Download Paper (Free):

• Brain Tumors
• Improved Convolutional Neural Network
• segmentation
• privacy-preserving
• BRATS
• Figshare
• Generative Adversarial Network

Abstract

Brain tumors, characterized by uncontrollable cellular growths, are a significant global health challenge. Navigating the complexities of tumor identification due to their varied dimensions and positions, our research introduces enhanced methods for precise detection. Utilizing advanced learning techniques, we've improved early identification by preprocessing clinical dataset-derived images, augmenting them via a Generative Adversarial Network, and applying an Improved Privacy-Preserving Collaborative Convolutional Neural Network (IPC-CNN) for segmentation. Recognizing the critical importance of data security in today's digital era, our framework emphasizes the preservation of patient privacy. We evaluated the performance of our proposed model on the Figshare and BRATS 2018 datasets. By facilitating a collaborative model training environment across multiple healthcare institutions, we harness the power of distributed computing to securely aggregate model updates, ensuring individual data protection while leveraging collective expertise. Our IPC-CNN model achieved an accuracy of 99.40%, marking a notable advancement in brain tumor classification and offering invaluable insights for both the medical imaging and machine learning communities.

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