Optimize Weight sharing for Aggregation Model in Federated Learning Environment of Brain Tumor classification
DOI:
https://doi.org/10.29304/jqcm.2022.14.3.989Keywords:
Federated learning, Medical images, Brain tumor, Classification, weight aggregationAbstract
Clinical diagnosis and therapy of brain tumors are greatly aided by proper classification of the tumors. Brain tumors can be diagnosed more quickly and accurately if radiologists use deep learning to help the specialist and doctors examine the enormous volume of brain MRI Images. Large datasets are required in training process, and whole of such data must be centralized for be handled by such techniques. It is sometimes impossible to collect and distribute patient data on a centralized data server because of medical data privacy regulations. In this paper, federated learning (FL) is proposed, in which data is non-shareable because of patient privacy issues. Using the FL approach, we have proposed two methods of aggregation; first, which concerns ranking the weight percentage of each client, and Second average weights method. and to evaluate the suggested model, we have compared the performance of the ranking weights percentage method with the average weights of proposed CNN and pre-training (VGG-16) in the FL environment in addition to SVM and VGG-16 . The experiments result was applied on two datasets, it shows our model accuracy result is very effective when using the ranking weight percentage method as compared with other methods, it achieves accuracy (98%) on datasets (BT_large-1c) and achieve (97.14%) on the dataset (BT-large-2c).
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References
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[2] Alfoudi, A., Alsaeedi, A., Abed, M., Otebolaku, A., & Razooqi, Y. (2021). Palm Vein Identification Based on Hybrid Feature Selection Model. International Journal of Intelligent Engineering and Systems, 14(5), 469
[3] J. Kang, Z. Ullah, and J. Gwak, “Mri-based brain tumor classification using ensemble of deep features and machine learning classifiers,” Sensors, vol. 21, no. 6, pp. 1–21, 2021, doi: 10.3390/s21062222.
[4] M. Aledhari, R. Razzak, R. M. Parizi, and F. Saeed, “Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Access, vol. 8, pp. 140699–140725, 2020, doi: 10.1109/ACCESS.2020.3013541.
[5] A. Qayyum, K. Ahmad, M. A. Ahsan, A. Al-Fuqaha, and J. Qadir, “Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge,” Jan. 2021, [Online]. Available: http://arxiv.org/abs/2101.07511.
[6] M. Khodak, T. Li, L. Li, M.-F. Balcan, V. Smith, and A. Talwalkar, “Weight-Sharing for Hyperparameter Optimization in Federated Learning,” Icml. 2020.
[7] K. Zhang, S. M. Yiu, and L. C. K. Hui, “A Light-Weight Crowdsourcing Aggregation in Privacy-Preserving Federated Learning System,” Proc. Int. Jt. Conf. Neural Networks, 2020, doi: 10.1109/IJCNN48605.2020.9206841.
[8] K. Wei et al., “Federated Learning with Differential Privacy: Algorithms and Performance Analysis,” IEEE Trans. Inf. Forensics Secur., vol. 15, pp. 3454–3469, 2020, doi: 10.1109/TIFS.2020.2988575.
[9] S. Silva et al., Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data; Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data. 2019.
[10] A. G. Roy, S. Siddiqui, S. Pölsterl, N. Navab, and C. Wachinger, “BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning,” pp. 1–9, 2019, [Online]. Available: http://arxiv.org/abs/1905.06731.
[11] P. Modiya and S. Vahora, “Brain Tumor Detection Using Transfer Learning with Dimensionality Reduction Method,” International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 2. pp. 201–206, 2022.
[12] S. K. V. Rao and B. Lingappa, “Image analysis for MRI based brain tumour detection using hybrid segmentation and deep learning classification technique,” International Journal of Intelligent Engineering and Systems, vol. 12, no. 5. pp. 53–62, 2019, doi: 10.22266/ijies2019.1031.06.
[13] T. Saba, A. Sameh Mohamed, M. El-Affendi, J. Amin, and M. Sharif, “Brain tumor detection using fusion of hand crafted and deep learning features,” Cogn. Syst. Res., vol. 59, pp. 221–230, Jan. 2020, doi: 10.1016/j.cogsys.2019.09.007.
[14] Hamzah Abed, Mohammed, Lamia Abed Noor Muhammed, and Sarah Hussein Toman. "Diabetic Retinopathy Diagnosis based on Convolutional Neural Network." Journal of Physics: Conference Series. Vol. 1999. No. 1. IOP Publishing, 2021.
[15] Al-Asfoor, M., Abed, M.H.. Deep Learning Approach for COVID-19 Diagnosis Using X-Ray Images. In: Ullah, A., Anwar, S., Rocha, Á., Gill, S. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 350. Springer, Singapore.(2022)https://doi.org/10.1007/978-981-16-7618-5_14
[16] Mahlool, D.H., Abed, M.H. (2022). A Comprehensive Survey on Federated Learning: Concept and Applications. In: Shakya, S., Ntalianis, K., Kamel, K.A. (eds) Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies, vol 126. Springer, Singapore. https://doi.org/10.1007/978-981-19-2069-1_37
[17] L. Li, Y. Fan, M. Tse, and K. Y. Lin, “A review of applications in federated learning,” Comput. Ind. Eng., vol. 149, no. September, 2020, doi: 10.1016/j.cie.2020.106854.
[18] B. Brik, A. Ksentini, and M. Bouaziz, “Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems,” IEEE Access, vol. 8, pp. 53841–53849, 2020, doi: 10.1109/ACCESS.2020.2981430.
[19] G. Natesan and A. Chokkalingam, “Opposition learning-based grey wolf optimizer algorithm for parallel machine scheduling in cloud environment,” International Journal of Intelligent Engineering and Systems, vol. 10, no. 1. pp. 186–195, 2017, doi: 10.22266/ijies2017.0228.20.
[20] W. Y. B. Lim et al., “Federated Learning in Mobile Edge Networks: A Comprehensive Survey,” IEEE Commun. Surv. Tutorials, vol. 22, no. 3, pp. 2031–2063, 2020, doi: 10.1109/COMST.2020.2986024.
[21] P. Afshar, A. Mohammadi, and K. N. Plataniotis, “Concordia Institute for Information Systems Engineering , Concordia University , Montreal , QC , Canada Department of Electrical and Computer Engineering , University of Toronto , Toronto , ON , Canada Emails : { p afs , arashmoh } @ encs . concordia . ca,” 2018 25th IEEE Int. Conf. Image Process., pp. 3129–3133, 2018.
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Published
2022-08-12
How to Cite
Mahlool, D. H., & Abed, M. H. (2022). Optimize Weight sharing for Aggregation Model in Federated Learning Environment of Brain Tumor classification. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(3), Comp Page 76–87. https://doi.org/10.29304/jqcm.2022.14.3.989
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Computer Articles
