## 4 Background Study
### Literature Review
Trained on 833 ADNI MRI scans, the model achieved an AUC of 95.92% for AD vs. NC, 88.72% for MCI vs. NC, and 85.35% for AD vs. MCI, outperforming SS-GAN and triple-GAN baselines by a notable margin. THS-GAN also proved more data-efficient, delivering strong results with fewer labeled samples. Limitations include sensitivity to TT-rank tuning and GSP block positioning. [PERSON] and [PERSON] [34] developed CT-GAN, an innovative deep learning model that blends brain structure and function by combining DTI and rs-fMRI scans to improve Alzheimer's diagnosis. By using attention-based transformers and GANs, the model effectively captured complex brain patterns and outperformed existing methods with accuracies of 94.44% (AD vs. NC), 93.55% (LMCI vs. NC), and 92.68% (EMCI vs. NC). It also highlighted key brain regions like the hippocampus and precuneus. However, the study was limited by a small dataset and reliance on predefined brain regions. [PERSON] and [PERSON] [35] introduced WGANGP-DTL, a classification framework combining Wasserstein GAN with Gradient Penalty and Deep Transfer Learning for Alzheimer's detection using 3D MRI scans. It uses WGANGP for data augmentation, 3 DS-FCM for segmentation, Inception v3 for feature extraction, and a Deep Belief Network for classification. Tested on 13,500 augmented images, the model achieved 99.70% accuracy, 99.09% sensitivity, and 99.82% specificity, outperforming other deep learning models. While highly effective, the method depends on extensive preprocessing and fine-tuning. [PERSON] et al. [36] introduced BPGAN, a 3D BicycleGAN-based model that synthesizes PET scans from MRI to tackle missing modality issues in AD diagnosis. Trained on 1732 MRI-PET pairs, it outperformed existing methods with strong image quality (e.g., SSIM 0.7294) and boosted AD classification accuracy by 1-4%, reaching 85.03% on Dataset-B. While effective, the model's reliance on complex preprocessing and limited diagnostic improvement suggest future work should explore adaptive ROI localization and broader clinical use. [PERSON] et al. [37] developed ReMiND, a diffusion model (DDPM)-based method to generate missing 3D MRI scans in longitudinal Alzheimer's studies. Using past or past-and-future scans, it outperformed autoencoders and simple methods in preserving brain structure, achieving SSIM of 0.895 and PSNR of 28.96 dB. However, it currently relies on fixed scan intervals and only nearby timepoints. Future work could include multiple timepoints and multimodal data for improved tracking of disease progression. [PERSON] et al. [38] proposed a wavelet-guided diffusion model to enhance low-resolution MRI scans, aiming to improve Alzheimer's diagnosis. Using a Wavelet U-Net with DDPM, the model achieved SSIM of 0.8201, PSNR of 27.15, and FID of 13.15, also improving classification accuracy. However, it is computationally intensive and limited to T1-weighted MRIs. Future work could expand to multi-modal and longitudinal data for broader clinical use. [PERSON] et al. (2019) introduced a deep learning approach that combines CNNs with GANs to boost Alzheimer's detection from MRI scans. By generating synthetic images and using filters to enhance image quality, their method achieved a strong 96% accuracy outperforming standard CNN models. Still, the study was limited by a small number of real moderate AD cases and the lack of other imaging types like PET or fMRI. [PERSON] et al. (2019) developed a deep learning method to distinguish AD, FTD, and healthy individuals using structural MRI. By combining brain atrophy data with grading maps from an ensemble of 3D U-Nets, their model achieved 86% accuracy and offered interpretable results. While it generalized well to other datasets, it is resource-intensive and limited to baseline MRI. Future work could include multi-modal and longitudinal data to better track disease progression. [PERSON] et al. (2019) combined MRI and PET scans with GAN-generated synthetic data to improve early Alzheimer's detection.