## 4 Background Study
### Literature Review
While promising, its complexity and need for fine-tuning limit scalability. Future work could focus on adding multi-modal and longitudinal data to boost clinical usefulness. [PERSON] et al. (2023) [43] introduced a conditional diffusion model to generate synthetic MRI slices for Alzheimer's diagnosis, helping to balance limited and uneven datasets. Using diagnostic labels to guide image creation, the model improved classification performance achieving 74.73% accuracy and an AUC of 0.8590, outperforming GAN-based methods. However, it used a small, static dataset and lacked specificity reporting. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Technique/Method** & **Model** & **Results** \\ \hline \multirow{3}{*}{[51]} & GAN-based data & GAN for data & Accuracy: 88.67\% (1:0), 87.17\% (9:1), \\  & generation + EfficientNet & augmentation + & 82.50\% (8:2), 80.17\% (7:3); \\  & for multistage classification & EfficientNet CNN & Recall/Sensitivity/Specificity not \\ \hline \multirow{3}{*}{[52]} & Conditional Latent & & SSIM: 0.924, PSNR: 33.35, CBF error: 1.07 \\  & Diffusion Model (LDM) for & Conditional LDM + ML & \(\pm\) 2.12 mL/100 g/min; AUC: 0.75 \\  & M0 image synthesis from & classifier & (Siemens), 0.90 (GE) in AD vs. CN \\  & Siemens PASL & & classification \\ \hline \multirow{3}{*}{[53]} & Multi-modal conditional & & \\  & diffusion model for & Conditional Diffusion & \\  & image-to-image translation & Model + U-Net & PSNR: 31.99 dB, SSIM: 0.75, FID: 11.43 \\  & (prognosis prediction) & & \\ \hline \multirow{3}{*}{[54]} & GAN for synthetic MRI & & Validation accuracy improved from \\  & image generation + & GAN + EfficientNet & 78.48\% to 85.11\%, training accuracy \\  & EfficientNet for multi-stage & & from 90.16\% to 98.68\% with GAN data \\  & classification & & \\ \hline \multirow{3}{*}{[55]} & Dual GAN + Pyramid & Dual GAN with Pyramid & Accuracy: 98.87\%, Recall/Sensitivity: 95.67\%, Specificity: 98.78\%, Precision: 99.78\%, F1-score: 99.67\% \\  & Attention + CNN & Attention and CNN & 99.78\%, F1-score: 99.67\% \\ \hline \multirow{3}{*}{[56]} & Prior-information-guided & & SSIM: 92.49\% (A\(\beta\)), 91.44\% (Tau); PSNR: 26.38 dB (A\(\beta\)), 27.78 dB (Tau); AUC: 90.74\%, F1: 82.74\% (A\(\beta\)); AUC: 90.02\%, F1: 76.67\% (Tau) \\ \hline \multirow{3}{*}{[57]} & Hybrid of Deep & & Accuracy: 99.22\%, Precision: 99.01\%, \\  & Super-Resolution GAN & & Recall: 99.01\%, F1-score: 99.01\%, AUC: \\  & (DSR-GAN) for image & DSR-GAN + CNN & 100\%, PSNR: 29.30 dB, SSIM: 0.847, \\  & enhancement + CNN for & & MS-SSIM: 96.39\% \\  & classification & & \\ \hline \multirow{3}{*}{[58]} & Bidirectional Graph GAN & & \\  & (BG-GAN) + Inner Graph & & Accuracy \(>\) 96\%, Precision/Recall/F1 \(\approx\) \\ \cline{1-1}  & Convolution Network + & BG-GAN + InnerGCN & 0.98–1.00, synthetic data outperformed \\ \cline{1-1}  & & & real in classification \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table} 
Table 3: _Cont._[PERSON] et al. (2024) [44] introduced a diffusion-based MRI super-resolution model (d3T) to enhance Alzheimer's and MCI diagnosis by upgrading 1.5 T scans to 3 T quality. The improved images boosted diagnostic accuracy to 92.3% and helped predict MCI-to-AD conversion more reliably. While the results are promising, the model was tested only on ADNI data and lacks validation on higher-resolution scans or multi-center datasets. Future work could explore 3 T-to-7 T enhancement and integrate multi-modal imaging for broader clinical use. [PERSON] et al. (2024) [45] combined InceptionV3 with GANs to improve Alzheimer's diagnosis from MRI scans, especially in imbalanced datasets. By generating synthetic images to boost underrepresented classes, their model achieved an AUC of 87% and showed better accuracy for early-stage AD. However, it was limited to one dataset and lacked multi-modal input. Future work should be tested on diverse datasets and include other imaging types like PET for more reliable diagnosis. [PERSON] et al.