Alzheimer’s Disease Detection Using Vision Transformers: A survey
DOI:
https://doi.org/10.29304/jqcsm.2025.17.22229Keywords:
Alzheimer's disease, Vision transformer, Optimization, Deep Learning, ViTAbstract
Alzheimer's disease is a progressive neurodegenerative disorder that primarily affects individuals aged 65 and older, leading to irreversible memory loss and cognitive decline. Early detection plays a critical role in managing the disease and improving patient outcomes. In recent years, numerous studies have investigated the development of automated systems to identify the stages of Alzheimer's disease using advanced deep learning methods. This paper provides a structured literature review focused on the use of Vision Transformers (ViTs) and metaheuristic optimization algorithms for early diagnosis. The reviewed studies demonstrate that ViT-based models outperform traditional approaches in extracting spatial and temporal features from brain imaging data, achieving classification accuracies exceeding 96% on widely used datasets such as ADNI and OASIS. Additionally, the review addresses key challenges in processing 3D medical images and highlights ongoing efforts to develop hybrid architectures that integrate the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers (ViT). The paper also explores how collaborative learning strategies can enhance model training while preserving patient privacy, making these techniques more suitable for real-world clinical applications.
Downloads
References
A. L. Sosa-Ortiz, I. Acosta-Castillo, and M. J. Prince, “Epidemiology of Dementias and Alzheimer’s Disease,” Arch. Med. Res., vol. 43, no. 8, pp. 600–608, Nov. 2012, doi: 10.1016/j.arcmed.2012.11.003.
E. R. Danielsen, Magnetic Resonance Spectroscopy Diagnosis of Neurological Diseases, 1st ed. CRC Press, 1999. doi: 10.1201/9781482270105.
P. Celsis, “Age-related cognitive decline, mild cognitive impairment or preclinical Alzheimer’s disease?,” Ann. Med., vol. 32, no. 1, pp. 6–14, Jan. 2000, doi: 10.3109/07853890008995904.
R. C. Petersen, “Mild Cognitive Impairment:,” Contin. Lifelong Learn. Neurol., vol. 22, no. 2, Dementia, pp. 404–418, Apr. 2016, doi: 10.1212/CON.0000000000000313.
F. T. Hane, M. Robinson, B. Y. Lee, O. Bai, Z. Leonenko, and M. S. Albert, “Recent Progress in Alzheimer’s Disease Research, Part 3: Diagnosis and Treatment,” J. Alzheimer’s Dis., vol. 57, no. 3, pp. 645–665, Apr. 2017, doi: 10.3233/JAD-160907.
T. Gómez-Isla, J. L. Price, D. W. McKeel Jr., J. C. Morris, J. H. Growdon, and B. T. Hyman, “Profound Loss of Layer II Entorhinal Cortex Neurons Occurs in Very Mild Alzheimer’s Disease,” J. Neurosci., vol. 16, no. 14, pp. 4491–4500, Jul. 1996, doi: 10.1523/JNEUROSCI.16-14-04491.1996.
A. Khvostikov, K. Aderghal, J. Benois-Pineau, A. Krylov, and G. Catheline, “3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies,” 2018, arXiv. doi: 10.48550/ARXIV.1801.05968.
A. W. Salehi, P. Baglat, B. B. Sharma, G. Gupta, and A. Upadhya, “A CNN Model: Earlier Diagnosis and Classification of Alzheimer Disease using MRI,” in 2020 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India: IEEE, Sep. 2020, pp. 156–161. doi: 10.1109/ICOSEC49089.2020.9215402.
E. N. Marzban, A. M. Eldeib, I. A. Yassine, Y. M. Kadah, and for the Alzheimer’s Disease Neurodegenerative Initiative, “Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks,” PLOS ONE, vol. 15, no. 3, p. e0230409, Mar. 2020, doi: 10.1371/journal.pone.0230409.
A. Punjabi, A. Martersteck, Y. Wang, T. B. Parrish, A. K. Katsaggelos, and and the Alzheimer’s Disease Neuroimaging Initiative, “Neuroimaging modality fusion in Alzheimer’s classification using convolutional neural networks,” PLOS ONE, vol. 14, no. 12, p. e0225759, Dec. 2019, doi: 10.1371/journal.pone.0225759.
G. W. Kidder and C. W. Montgomery, “Oxygenation of frog gastric mucosa in vitro,” Am. J. Physiol., vol. 229, no. 6, pp. 1510–1513, Dec. 1975, doi: 10.1152/ajplegacy.1975.229.6.1510.
A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” 2020, arXiv. doi: 10.48550/ARXIV.2010.11929.
X. Li et al., “Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds,” Int. J. Netw. Dyn. Intell., pp. 93–116, Mar. 2023, doi: 10.53941/ijndi0201006.
S. Cuenat and R. Couturier, “Convolutional Neural Network (CNN) vs Vision Transformer (ViT) for Digital Holography,” in 2022 2nd International Conference on Computer, Control and Robotics (ICCCR), Shanghai, China: IEEE, Mar. 2022, pp. 235–240. doi: 10.1109/ICCCR54399.2022.9790134.
M. Filipiuk and V. Singh, “Comparing Vision Transformers and Convolutional Nets for Safety Critical Systems.,” in Safeai@ aaai, 2022. Accessed: Apr. 03, 2025. [Online]. Available: https://ceur-ws.org/Vol-3087/paper_31.pdf
X. Chen, S. Xie, and K. He, “An Empirical Study of Training Self-Supervised Vision Transformers,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada: IEEE, Oct. 2021, pp. 9620–9629. doi: 10.1109/ICCV48922.2021.00950.
L. Yuan et al., “Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada: IEEE, Oct. 2021, pp. 538–547. doi: 10.1109/ICCV48922.2021.00060.
Y. Lyu, X. Yu, D. Zhu, and L. Zhang, “Classification of Alzheimer’s Disease via Vision Transformer: Classification of Alzheimer’s Disease via Vision Transformer,” in Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, Corfu Greece: ACM, Jun. 2022, pp. 463–468. doi: 10.1145/3529190.3534754.
Z. Zhang and F. Khalvati, “Introducing Vision Transformer for Alzheimer’s Disease classification task with 3D input,” 2022, arXiv. doi: 10.48550/ARXIV.2210.01177.
S. Sarraf, A. Sarraf, D. D. DeSouza, J. A. E. Anderson, M. Kabia, and The Alzheimer’s Disease Neuroimaging Initiative, “OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer’s Disease Using Resting-State fMRI and Structural MRI Data,” Brain Sci., vol. 13, no. 2, p. 260, Feb. 2023, doi: 10.3390/brainsci13020260.
H. Shin, S. Jeon, Y. Seol, S. Kim, and D. Kang, “Vision Transformer Approach for Classification of Alzheimer’s Disease Using 18F-Florbetaben Brain Images,” Appl. Sci., vol. 13, no. 6, p. 3453, Mar. 2023, doi: 10.3390/app13063453.
M. H. Alshayeji, “Alzheimer’s disease detection and stage identification from magnetic resonance brain images using vision transformer,” Mach. Learn. Sci. Technol., vol. 5, no. 3, p. 035011, Sep. 2024, doi: 10.1088/2632-2153/ad5fdc.
M. F. Almufareh, S. Tehsin, M. Humayun, and S. Kausar, “Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease,” Healthcare, vol. 11, no. 20, p. 2763, Oct. 2023, doi: 10.3390/healthcare11202763.
U. Khatri and G.-R. Kwon, “Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer’s Disease Progression Using 18F-FDG PET,” Bioengineering, vol. 10, no. 10, p. 1225, Oct. 2023, doi: 10.3390/bioengineering10101225.
M. Zaabi, M. I. Khedher, and M. A. El-Yacoubi, “Improving Alzheimer’s Diagnosis Using Vision Transformers and Transfer Learning,” in 2024 16th International Conference on Human System Interaction (HSI), Paris, France: IEEE, Jul. 2024, pp. 1–6. doi: 10.1109/HSI61632.2024.10613527.
M. Odusami, R. Maskeliūnas, and R. Damaševičius, “Pixel-Level Fusion Approach with Vision Transformer for Early Detection of Alzheimer’s Disease”.
U. Khatri and G.-R. Kwon, “RMTnet: Recurrence meet Transformer for Alzheimer’s disease diagnosis using FDG-PET,” Jan. 22, 2024, Preprints. doi: 10.36227/techrxiv.170594596.62106929/v1.
X. Xing, G. Liang, Y. Zhang, S. Khanal, A.-L. Lin, and N. Jacobs, “Advit: Vision Transformer On Multi-Modality Pet Images For Alzheimer Disease Diagnosis,” in 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India: IEEE, Mar. 2022, pp. 1–4. doi: 10.1109/ISBI52829.2022.9761584.
P. Sherwani, P. Nandhakumar, P. Srivastava, J. Jagtap, V. Narvekar, and H. R, “Comparative Analysis of Alzheimer’s Disease Detection via MRI Scans Using Convolutional Neural Network and Vision Transformer,” in 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), Chennai, India: IEEE, Jan. 2023, pp. 1–9. doi: 10.1109/ICECONF57129.2023.10084260.
N. J. Dhinagar, S. I. Thomopoulos, E. Laltoo, and P. M. Thompson, “Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer’s Disease Detection,” in 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia: IEEE, Jul. 2023, pp. 1–6. doi: 10.1109/EMBC40787.2023.10341190.
R. C. Poonia and H. A. Al-Alshaikh, “Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0,” Comput. Biol. Med., vol. 179, p. 108874, Sep. 2024, doi: 10.1016/j.compbiomed.2024.108874.
S. M. A. H. Shah, M. Q. Khan, A. Rizwan, S. U. Jan, N. A. Samee, and M. M. Jamjoom, “Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT),” Pattern Anal. Appl., vol. 27, no. 3, p. 76, Sep. 2024, doi: 10.1007/s10044-024-01297-6.
Y. Yin, W. Jin, J. Bai, R. Liu, and H. Zhen, “SMIL-DeiT:Multiple Instance Learning and Self-supervised Vision Transformer network for Early Alzheimer’s disease classification,” in 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy: IEEE, Jul. 2022, pp. 1–6. doi: 10.1109/IJCNN55064.2022.9892524.
M. A. Aghdam, S. Bozdag, and F. Saeed, “Pvtad: Alzheimer’s Disease Diagnosis Using Pyramid Vision Transformer Applied to White Matter of T1-Weighted Structural Mri Data,” in 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece: IEEE, May 2024, pp. 1–4. doi: 10.1109/ISBI56570.2024.10635541.
F. Huang and A. Qiu, “Ensemble Vision Transformer for Dementia Diagnosis,” IEEE J. Biomed. Health Inform., vol. 28, no. 9, pp. 5551–5561, Sep. 2024, doi: 10.1109/JBHI.2024.3412812.
X. Mu et al., “Alzheimer Classification Based on Convolutional Neural Network and Vision Transformer,” in 2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), Chengdu, China: IEEE, Nov. 2023, pp. 329–334. doi: 10.1109/ICICML60161.2023.10424819.
A. Sen, U. Sen, and S. Roy, “A Comparative Analysis on Metaheuristic Algorithms Based Vision Transformer Model for Early Detection of Alzheimer’s Disease,” in 2023 IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN), Dec. 2023, pp. 200–205. doi: 10.1109/CICN59264.2023.10402213.
A. Becker, “NSGA-VIT: AN EVOLUTIONARY APPROACH TO VISION TRANSFORMER ARCHITECTURE DESIGN”.
J. Zhang et al., “EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm,” Int. J. Comput. Vis., vol. 132, no. 9, pp. 3509–3536, Sep. 2024, doi: 10.1007/s11263-024-02034-6.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Nasrallah Asem AL-Sultani, Alaa Taima Albu-Salih, Osama Majeed Hilal
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
