Early Detection of Alzheimer’s Disease Using Generative Models: A Review of GANs and Diffusion Models in Medical Imaging
Open accessAbstract
Alzheimer’s disease (AD) is a progressive, non-curable neurodegenerative disorder that poses persistent challenges for early diagnosis due to its gradual onset and the difficulty in distinguishing pathological changes from normal aging. Neuroimaging, particularly MRI and PET, plays a key role in detection; however, limitations in data availability and the complexity of early structural biomarkers constrain traditional diagnostic approaches. This review investigates the use of generative models specifically Generative Adversarial Networks (GANs) and Diffusion Models as emerging tools to address these challenges. These models are capable of generating high-fidelity synthetic brain images, augmenting datasets, and enhancing machine learning performance in classification tasks. The review synthesizes findings across multiple studies, revealing that GAN-based models achieved diagnostic accuracies up to 99.70%, with image quality metrics such as SSIM reaching 0.943 and PSNR up to 33.35 dB. Diffusion Models, though relatively new, demonstrated strong performance with up to 92.3% accuracy and FID scores as low as 11.43. Integrating generative models with convolutional neural networks (CNNs) and multimodal inputs further improved diagnostic reliability. Despite these advancements, challenges remain, including high computational demands, limited interpretability, and ethical concerns regarding synthetic data. This review offers a comprehensive perspective to inform future AI-driven research in early AD detection.
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Few reviewed studies considered how digital literacy, age, or socioeconomic status may hinder access and engagement with mHealth interventions.
“Few studies considered how digital literacy, age, or socioeconomic status may hinder access and engagement with mHealth interventions [42,43].”
From Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review (2025) · cites this paper as [43]
Section 4.2 reviews generative models for Alzheimer’s diagnosis using neuroimaging, while the Discussion and Conclusion addresses limitations such as generalizability and clinical validation. Searches for digital literacy, socioeconomic barriers, and mHealth access or engagement found no passage addressing the attributed claim. (LLM, conf=0.76)
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Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review
Khandoker, F., & Grigsby, T. J. (2025) · Applied Sciences · doi:10.3390/app151910567 · Cited by 2 · see citing works
- Authors
- Alam, M. M., & Latifi, S.
- Affiliations
- University of Nevada, Las Vegas (US)
- Corresponding author
- Latifi, Shahram
- Venue
- Algorithms
- Publisher
- MDPI AG
- Published
- 2025-06-24
- Volume
- 18
- Issue
- 7
- Pages
- 434-434
- ISSN
- 1999-4893 (ISSN-L)
- OpenAlex
- W4412451508
- Cited by
- 17
- References
- 61
- Citations by year
- 2026: 13 · 2025: 4
- FWCI
- 11.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Algorithms (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- Alzheimer's disease, early diagnosis, generative adversarial networks, diffusion models, medical image augmentation, magnetic resonance imaging, PET, multimodal neuroimaging, convolutional neural networks, interpretability
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