Explainable Artificial Intelligence Analysis of Transfer Learning Models for Alzheimer’s Disease MRI Classification

Authors

  • Dea Amanda Salsabila Universitas 17 Agustus 1945 Surabaya
  • Ghaluh Indah Permata Sari National Taiwan University of Science and Technology
  • Fajar Astuti Hermawati Universitas 17 Agustus 1945 Surabaya

DOI:

https://doi.org/10.30996/jitcs.133060

Keywords:

Alzheimer’s disease, clinical decision support, deep learning, EAI, explainable artificial intelligence, magnetic resonance imaging, transfer learning

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder that leads to cognitive decline and requires early and accurate diagnosis to slow disease progression. Magnetic resonance imaging (MRI) is widely used to detect structural brain changes associated with Alzheimer’s disease; however, manual interpretation of MRI scans is time-consuming and subject to observer variability. Deep learning approaches have shown strong potential in automated MRI analysis, but their black-box nature limits clinical trust and interpretability. This study proposes a transfer learning–based deep learning framework for Alzheimer’s disease classification, complemented by explainable artificial intelligence (XAI) techniques to analyze model predictions. A pretrained VGG16 model is employed to classify MRI images into four cognitive impairment categories: no impairment, very mild impairment, mild impairment, and moderate impairment. To enhance transparency, Grad-CAM, Saliency Maps, and Guided Grad-CAM are applied to visualize brain regions that contribute most to model predictions. Experimental results demonstrate that the proposed approach achieves 95.41% accuracy, indicating that a well-balanced network architecture combined with integrated explainability techniques leads to effective, interpretable classification. The visual explanations highlight clinically meaningful brain regions that align with known Alzheimer’s disease–related structural changes. These findings suggest that combining deep transfer learning with explainable artificial intelligence can provide accurate and interpretable decision support for Alzheimer’s disease diagnosis. This study is limited by the use of a single publicly available dataset and two-dimensional MRI slices, which may affect generalizability across clinical environments.

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Author Biographies

Dea Amanda Salsabila, Universitas 17 Agustus 1945 Surabaya

Department of Informatics Engineering

Ghaluh Indah Permata Sari, National Taiwan University of Science and Technology

Department of Computer Science and Information Engineering

Fajar Astuti Hermawati, Universitas 17 Agustus 1945 Surabaya

Department of Informatics Engineering

References

AbdelAziz, N. M., Said, W., AbdelHafeez, M. M., & Ali, A. H. (2024). Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AI. Frontiers in Artificial Intelligence, 7. doi:https://doi.org/10.3389/frai.2024.1456069

Aderghal, K., Benois-Pineau, J., Afdel, K., & Gwenaëlle, C. (2017). FuseMe: Classification of sMRI images by fusion of Deep CNNs in 2D+ε projections. CBMI '17: Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing. Florence, Italy: ACM. doi:https://doi.org/10.1145/3095713.3095749

Ali, M. U., Hussain, S. J., Khalid, M., Farrash, M., Lahza, H. F., & Zafar, A. (2024). MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature. Bioengineering, 11(11). doi:https://doi.org/10.3390/bioengineering11111076

Basaia, S., Agosta, F., Wagner, L., Canu, E., Magnani, G., Santangelo, R., & Filippi, M. (2019). Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage: Clinical, 21. doi:https://doi.org/10.1016/j.nicl.2018.101645

Bron, E. E., Klein, S., Papma, J. M., Jiskoot, L. C., Venkatraghavan, V., Linders, J., . . . W. (2021). Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer’s disease. NeuroImage: Clinical, 31. doi:https://doi.org/10.1016/j.nicl.2021.102712

Chattopadhyay, T., Joshy, N. A., Jagad, C., Gleave, E. J., Thomopoulos, S. I., Feng, Y., . . . Thompson, P. M. (2024, September 17). Comparison of Explainable AI Models for MRI-based Alzheimer’s Disease Classification. doi:https://doi.org/10.1101/2024.09.17.613560

De Santi, L. A., Pasini, E., Santarelli, M. F., Genovesi, D., & Positano, V. (2023). An Explainable Convolutional Neural Network for the Early Diagnosis of Alzheimer’s Disease from 18F-FDG PET. Journal of Digital Imaging, 36, 189-203. doi:https://doi.org/10.1007/s10278-022-00719-3

El-Assy, A. M., Amer, H. M., Ibrahim, H. M., & Mohamed, M. A. (2024). A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data. Scientific Reports, 14. doi:https://doi.org/10.1038/s41598-024-53733-6

Islam, J., & Zhang, Y. (2018). Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks Original research Open access Published: 31 May 2018. Brain Informatics, 5. doi:https://doi.org/10.1186/s40708-018-0080-3

Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., . . . Ran, K. P. (2018). NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimer's and Dementia, 14(4), 535-562. doi:https://doi.org/10.1016/j.jalz.2018.02.018

Khosroshahi, M. T., Morsali, S., Gharakhanlou, S., Motamedi, A., Hassanbaghlou, S., Vahedi, H., . . . Jafarizadeh, A. (2025). Explainable Artificial Intelligence in Neuroimaging of Alzheimer’s Disease. Diagnostics, 15(5). doi:https://doi.org/10.3390/diagnostics15050612

Komal, R., Dhavakumar, P., Rahul, K., Jaswanth, B., & Preeth, R. (2025). Hybrid deep learning framework for magnetic resonance imaging-based classification of Alzheimer’s disease. Brain Network Disorders, 1(4), 239-249. doi:https://doi.org/10.1016/j.bnd.2025.06.002

Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., . . . Mika. (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet, 396, 413–46. doi:https://doi.org/10.1016/S0140-6736(20)30367-6

Mahmud, T., Barua, K., Habiba, S. U., Sharmen, N., Hossain, M. S., & Andersson, K. (2024). An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning. Diagnostics, 14(3). doi:https://doi.org/10.3390/diagnostics14030345

Odusami, M., Damaševičius, R., Milieškaitė-Belousovienė, E., & Maskeliūnas, R. (2024). Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization. Heliyon, 10. doi:https://doi.org/10.1016/j.heliyon.2024.e34402

Odusami, M., Maskeliūnas, R., Damaševičius, R., & Misra, S. (2023). Explainable Deep-Learning-Based Diagnosis of Alzheimer’s Disease Using Multimodal Input Fusion of PET and MRI Images. Journal of Medical and Biological Engineering, 43, 291-302. doi:https://doi.org/10.1007/s40846-023-00801-3

Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017). A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. NeuroImage, 155, 530-548. doi:https://doi.org/10.1016/j.neuroimage.2017.03.057

Samek, W., Wiegand, T., & Müller, K.-R. (2017, Aug 28). Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models. doi:https://doi.org/10.48550/arXiv.1708.08296

Sampath, R., & Baskar, M. (2024). Alzheimer's Disease Prediction Using Fly-Optimized Densely Connected Convolution Neural Networks Based on MRI Images. The Journal of Prevention of Alzheimer's Disease, 11(4), 1106-1121. doi:https://doi.org/10.14283/jpad.2024.66

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. International Journal of Computer Vision, 128, 336-359. doi:https://doi.org/10.1007/s11263-019-01228-7

Sheikh, F., Marouf, A. A., Rokne, J. G., & Alhajj, R. (2025). Lightweight Deep Learning Models with Explainable AI for Early Alzheimer’s Detection from Standard MRI Scans. Diagnostics, 15(21). doi:https://doi.org/10.3390/diagnostics15212709

Shuvo, S. S., Refat, S. R., Preotee, F. F., & Muhammad, T. (2025). Advanced CNN and Explainable AI Based Architecture for Interpretable Brain MRI Analysis. ICCA '24: Proceedings of the 3rd International Conference on Computing Advancements (pp. 319-326). Dhaka, Bangladesh: ACM. doi:https://doi.org/10.1145/3723178.3723220

Soladoye, A. A., Aderinto, N., Osho, D., & Olawade, D. B. (2025). Explainable machine learning models for early Alzheimer’s disease detection using multimodal clinical data. International Journal of Medical Informatics, 204. doi:https://doi.org/10.1016/j.ijmedinf.2025.106093

Sorour, S. E., El-Mageed, A. A., Albarrak, K. M., Alnaim, A. K., Wafa, A. A., & El-Shafeiy, E. (2024). Classification of Alzheimer’s disease using MRI data based on Deep Learning Techniques. Journal of King Saud University - Computer and Information Sciences, 36(2). doi:https://doi.org/10.1016/j.jksuci.2024.101940

Tjoa, E., & Guan, C. (2021). A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793-4813. doi:https://doi.org/10.1109/TNNLS.2020.3027314

Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-González, J., Routier, A., Bottani, S., . . . Colliot, O. (2020). Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. Medical Image Analysis, 63. doi:https://doi.org/10.1016/j.media.2020.101694

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Published

2026-01-28

How to Cite

Salsabila, D. A., Sari, G. I. P., & Hermawati, F. A. (2026). Explainable Artificial Intelligence Analysis of Transfer Learning Models for Alzheimer’s Disease MRI Classification. Journal of Information Technology and Cyber Security, 4(1), 1–15. https://doi.org/10.30996/jitcs.133060

Issue

Vol. 4 No. 1 (2026): January

Section

Research Article

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