## 4 Background Study
### Literature Review
(2024) [46] developed DeepCGAN, a deep learning model that uses cognitive test data over time rather than brain scans to detect Alzheimer's early. With a BiGRU-enhanced GAN design and advanced loss functions, it achieved impressive results: 97.32% accuracy and a near-perfect AUC of 99.51%. While powerful, the model is complex and resource-heavy. Future improvements could involve combining it with imaging data or simplifying the architecture for easier clinical use. [PERSON] et al. (2024) [47] developed a latent diffusion model (LDM-RR) to boost the clarity and accuracy of amyloid PET scans by generating high-resolution images using paired low-res PET and MRI data. Trained in synthetic examples and tested across major datasets like ADNI and OASIS-3, the model delivered strong results in a recovery coefficient of 0.96, better detection of amyloid buildup over time (\(p\) = 1.3 \(\times\) 10\({}^{-10}\)), and improved consistency across different PET tracers (r = 0.9411, \(p\) = 0.0421). Though standard metrics like accuracy and sensitivity were not detailed, the model clearly outperformed older methods. Its main drawbacks are high computational demands and reliance on synthetic data. The authors suggest future improvements like faster, self-supervised training and broader real-world validation. [PERSON] et al. (2024) [48] introduced a diffusion-based method to generate FDG PET scans from MRI T1 images for Alzheimer's diagnosis. Their two-way diffusion approach transforming MRI to PET via a diffusion and reconstruction process outperformed the simpler one-way method, achieving 82.19% accuracy and high sensitivity (95.19%) and specificity (92.98%). While effective, the method is computationally intensive. The authors suggest that combining diffusion with other generative models like GANs could enhance performance and efficiency. [PERSON] et al. (2024) [49] developed an interpretable diffusion-based method to generate 3D brain MRIs for Alzheimer's diagnosis. Using DDPMs and Latent Diffusion Models, they created realistic and even counterfactual scans imagining how an AD patient's brain might look if healthy. These synthetic images helped pre-train a 3D CNN, which achieved strong results on the ADNI dataset (AUC 0.87, sensitivity 88.9%, specificity 83.7%) and generalized well to OASIS. Despite the high performance, the approach is computationally heavy and may benefit from efficiency improvements and added clinical data in the future. [PERSON] et al. (2024) [50] compared GANs, VAEs, and diffusion models (DDIMs) to generate MRI scans for diagnosing Alzheimer's and Parkinson's diseases. Diffusion models delivered the best results of 80.84% accuracy for Alzheimer's and 92.42% for Parkinson's though they were slow to train. VAEs produced lower-quality images but still supported decent classification. The study highlights the potential of generative models to boost diagnostic accuracy with limited data, though future work should focus on making diffusion models faster and more efficient for clinical use. [PERSON] et al. (2024) [51] tackled the challenge of limited MRI data in Alzheimer's research by using GANs to generate synthetic images. These were combined with real scans to train an EfficientNet model for classifying AD, MCI, and NC stages. Even with less real data, the model maintained strong accuracy up to 88.67% with full data and 80.17% with just 70% real data. While GAN training had some stability issues and too much synthetic data slightly hurt performance, the approach proved that GANs can effectively boost diagnosis when data is scarce. [PERSON] et al. (2024) [52] addressed the issue of missing M0 calibration images in Siemens ASL MRI scans, which was key for measuring brain blood flow in Alzheimer's research by using a conditional latent diffusion model to generate them. Their model produced high-quality images (SSIM 0.924, PSNR \(\sim\)33.35) and accurate CBF values, aligning well with known disease patterns. It also showed solid classification performance, especially with GE data (AUC up to 0.90).