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Privacy-preserving collaborative AI for distributed deep learning with cross-sectional data

  • 1238: Recent Advances in Biometrics Based on Biomedical Information
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Abstract

Recent progress in Deep Learning (DL) has shown potential in intelligent healthcare applications, enhancing patients’ quality of life. However, improving DL precision requires a larger and diverse dataset, leading to privacy and confidentiality challenges when consolidating data at a centralized server. To address this, we propose a skin cancer detection method prioritizing patient information and privacy. "Skin-net," a novel Convolutional Neural Network (CNN) model, integrates progressively private Federated Learning (FL) for accurate classification of complex skin lesion images. FL ensures data confidentiality during model training. Skin-net achieves promising results, with 98.3%± accuracy, 98.8%± sensitivity, and 97.9%± specificity, while preserving data privacy. It offers an effective pathway for skin cancer analysis and image augmentation, mitigating privacy concerns in medical image analysis.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This Research is funded by Researchers Supporting Project Number (RSPD2023R947), King Saud University, Riyadh, Saudi Arabia.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab, 54000, Pakistan

    Saeed Iqbal & Adnan N. Qureshi

  2. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, Riyadh, Riyadh, 11543, Saudi Arabia

    Musaed Alhussein & Khursheed Aurangzeb

  3. Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, 27272, UAE

    Khalid Javeed

  4. Intelligent Mechatronics Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul, 05006, South Korea

    Rizwan Ali Naqvi

  5. Faculty of Arts, Society and Professional Studies, Newman University, Birmingham, Birmingham, United Kingdom

    Adnan N. Qureshi

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  1. Saeed Iqbal
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  2. Adnan N. Qureshi
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  3. Musaed Alhussein
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Correspondence to Saeed Iqbal.

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Iqbal, S., Qureshi, A.N., Alhussein, M. et al. Privacy-preserving collaborative AI for distributed deep learning with cross-sectional data. Multimed Tools Appl 83, 80051–80073 (2024). https://doi.org/10.1007/s11042-023-17202-y

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  • DOI: https://doi.org/10.1007/s11042-023-17202-y

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