Version 1
: Received: 1 May 2023 / Approved: 2 May 2023 / Online: 2 May 2023 (02:57:27 CEST)
Version 2
: Received: 4 May 2023 / Approved: 5 May 2023 / Online: 5 May 2023 (04:49:41 CEST)
Ghosh, T.; Palash, M.I.A.; Yousuf, M.A.; Hamid, M.A.; Monowar, M.M.; Alassafi, M.O. A Robust Distributed Deep Learning Approach to Detect Alzheimer’s Disease from MRI Images. Mathematics2023, 11, 2633.
Ghosh, T.; Palash, M.I.A.; Yousuf, M.A.; Hamid, M.A.; Monowar, M.M.; Alassafi, M.O. A Robust Distributed Deep Learning Approach to Detect Alzheimer’s Disease from MRI Images. Mathematics 2023, 11, 2633.
Ghosh, T.; Palash, M.I.A.; Yousuf, M.A.; Hamid, M.A.; Monowar, M.M.; Alassafi, M.O. A Robust Distributed Deep Learning Approach to Detect Alzheimer’s Disease from MRI Images. Mathematics2023, 11, 2633.
Ghosh, T.; Palash, M.I.A.; Yousuf, M.A.; Hamid, M.A.; Monowar, M.M.; Alassafi, M.O. A Robust Distributed Deep Learning Approach to Detect Alzheimer’s Disease from MRI Images. Mathematics 2023, 11, 2633.
Abstract
Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Many researches have been conducted to detect it from MRI images through various deep-learning approaches. However, the problem of the availability of medical data and the privacy of the patients still exists. To mitigate this issue in Alzheimer’s disease detection, we have implemented the federated approach which is found to be more efficient, robust, and consistent compared to the conventional approach. For this, we needed deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilized the publicly available two datasets (OASIS, and ADNI), and designed various cases to evaluate the performance of the federated approach. In almost all of them, the federated approach achieved better accuracy and sensitivity. In distinguishing between two classes equally, the capability of the models was better in the found model when trained with federated approach compared to the conventional approach. In this approach. MobileNet, which is a low-cost transfer learning architecture, achieved the highest 95.24%, 81.94%, and 83.97% accuracy in OASIS, ADNI, and merged (ADNI+OASIS) test set, which was way higher than the achieved performance in Conventional approach. Moreover, in this approach, only the weights of the model were shared which keeps the original MRI images in their respective hospital or institutions, mitigating the privacy concern in the healthcare domain.
Keywords
Federated Learning; Alzheimer’s Disease; medical imaging; MRI Image
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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