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DEEP TRANSFER LEARNING-BASED ENSEMBLE FRAMEWORK FOR ALZHEIMER’S DISEASE CLASSIFICATION USING MRI SCANS

AUTHORS:
Aaron Fernando
M Ganesh
Mentor
Mani Deepak Choudhry
Affiliation
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

Alzheimer is a progressive neural disease which leads to cognitive decline and memory loss as well. The important thing turns out to be making early diagnosis for a more effective medical care and which will improve patient management as well. MRI which is widely used to analyse the structural changes in brain with the early stage abnormalities like in Mild cognitive impairment (MCI), which are often very subtle and quite difficult to identify through general manual interpretation by researchers. Other existing deep learning based systems which rely on single models those suffer from overfitting, poor generalization due to the constrained medical imaging datasets. Now, to address these issues, our project proposes an easily understandable deep transfer learning-based ensemble model for an automated classi- fication of the Alzheimer’s disease stages, including Cognitively Normal, Mild Cognitive Impairment, and Alzheimer’s Disease. This system follows a proper end to end pipeline, like it starts with preprocessing the MRI scans so that the noise would be reduced and the content quality would be maintained. Then multiple pre-trained convolutional neural network architectures are then utilized to extract the features through transfer learning, which helps in effectively learning the brain structural patterns even with limited data availability.

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Fernando, A. & Ganesh, M. (2026). Deep Transfer Learning-Based Ensemble Framework for Alzheimer’s Disease Classification using MRI Scans. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.423

Fernando, Aaron, and M Ganesh. "Deep Transfer Learning-Based Ensemble Framework for Alzheimer’s Disease Classification using MRI Scans." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.423.

Fernando, Aaron, and M Ganesh. "Deep Transfer Learning-Based Ensemble Framework for Alzheimer’s Disease Classification using MRI Scans." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.423.

References
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3.Pellegrini, F. Ballerini, E. Hernandez, M. del C. Valde´s Herna´ndez,

4.M. Gonza´lez-Castro, and D. Rueckert, “Machine learning of neu- roimaging for assisted diagnosis of dementia: A systematic review,” Alzheimer’s & Dementia, vol. 14, no. 9, pp. 1230–1240, Sep. 2018. https://doi.org/10.1016/j.jalz.2018.06.3069

5.Tanveer, B. Richhariya, R. U. Khan, A. H. Rashid, P. Khanna, M. Prasad, and C.-T. Lin, “Machine learning techniques for the diagnosis of Alzheimer’s disease: A review,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 1s, pp. 1– 35, Apr. 2020. https://doi.org/10.1145/3344998

6.Jain, N. Jain, and P. Aggarwal, “Convolutional neural network based Alzheimer’s disease classification from MRI,” Cognitive Systems Research, vol. 57, pp. 147–159, Oct. 2019. https://doi.org/10.1016/j. cogsys.2019.04.001

7.Wang, Y. Shen, S. Wang, and Y. Liu, “Ensemble learning for early diagnosis of Alzheimer’s disease using structural MRI,” Neurocomput- ing, vol. 365, pp. 12–22, Nov. 2019. https://doi.org/10.1016/j.neucom.2019.06.047

8.Hon and N. M. Khan, “Towards Alzheimer’s disease classification through transfer learning,” in Proc. IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 1166–1169. https://ieeexplore.ieee.org/document/8217829

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10.Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics, and transfer learning,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285–1298, May 2016. https://ieeexplore.ieee.org/document/

 
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