## 4 Background Study
### Literature Review  
This section provides a comprehensive synthesis of notable research articles from 2013 to 2025 highlighting generative models, particularly GANs and diffusion models, in enhancing the early diagnosis of AD using various neuroimaging modalities. Table 2 highlights the medical image dataset being used in the research articles included in this. It also provides the modality and total number of participants used in their study. Table 3 provides an explanation of the different methods used by the researchers and their performance metrics according to the model used. Table 4 presents the models used in the reviewed literature highlighting their key challenges and limitations. [PERSON] et al. [21] developed a network diffusion model to simulate the prion-like spread of dementia across brain networks. By analyzing brain scans from healthy individuals, they predicted where atrophy would appear in Alzheimer's and frontotemporal dementia patients and their predictions closely matched real MRI data. The model performed impressively, with diagnostic accuracy exceeding an AUC of 0.90, even outperforming traditional PCA methods. However, it relied on a static brain map and a small sample, which may limit its flexibility. [PERSON] et al. [22] used an SVM-based method to detect early-stage Alzheimer's by analyzing DTI brain scans, focusing on white matter integrity and fiber pathways especially those linked to the thalamus. Their model achieved 100% accuracy, 100% sensitivity, and 100% specificity in both 10-fold cross-validation and independent testing. This highlights its strong potential for distinguishing MCI from healthy aging. However, its dependence on pre-defined seed regions and differences in imaging sources may limit generalizability. Future improvements could include standardizing imaging protocols and incorporating other biomarkers for broader clinical use. [PERSON] et al. [23] introduced a two-stage deep learning framework to improve Alzheimer's diagnosis using MRI and missing PET data. First, they used a 3D CycleGAN to synthesize PET scans from corresponding MRIs. Then, they applied a multi-modal learning model (LM3 IL) to classify AD and predict MCI conversion. Tested on ADNI datasets, their method  
Figure 7: PRISMA flow chart for study selection process. achieved strong performance 92.5% accuracy, 89.94% sensitivity, and 94.53% specificity for AD vs. HC classification. The generated PET scans were visually realistic and had a PSNR of 24.49. While effective, the method depends on careful image alignment and patch extraction. Future work could explore more flexible, end-to-end models for better generalization. [PERSON] et al. [24] proposed an unsupervised GAN-based method to detect Alzheimer's disease by reconstructing MRI slice sequences using WGAN-GP with L1 loss. Trained on healthy brain scans from the OASIS-3 dataset, the model identifies anomalies based on reconstruction errors. It achieved AUCs of 0.780 (CDR 0.5), 0.833 (CDR 1), and 0.917 (CDR 2), effectively detecting early to advanced AD stages. The approach requires no labeled pathological data, making it scalable, but it may overlook abnormalities outside selected brain regions. Future work could explore full-brain coverage and multimodal data for broader detection. [PERSON] et al. [25] proposed GANDALE, a GAN-based model that combines MRI-to-PET synthesis with AD classification using discriminator-adaptive loss fine-tuning. Trained end-to-end on the ADNI dataset, it achieved 85.2% accuracy for AD/CN, 78.7% for AD/MCI/CN (Precision: 0.83, Recall: 0.66), and 37.0% for AD/LMCI/EMCI/CN classification. While it outperformed baseline methods in multi-class tasks, binary performance was similar to CNN-only models. More advanced architecture and tuning could further enhance results. [PERSON] and [PERSON] [26] designed a GAN-based approach to generate synthetic brain PET images for different stages of Alzheimer's disease Normal Control (NC), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD).