## 4 Background Study
### Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Model** & **Challenges and Limitations** \\ \hline
[36] & ADNI (1732 scan-pairs, 873 subjects) & 3D BicycleGAN (BPGAN) with MCU Generator & High preprocessing complexity, marginal diagnostic gains, requires broader validation and adaptive ROI exploration. \\ \hline
[37] & ADNI & Denoising Diffusion Probabilistic Model (DDPM) with modified U-Net & Uses only adjacent timepoints; assumes fixed intervals; no classification; no sensitivity/specificity; computationally intensive. \\ \hline
[38] & ADNI2 & Wavelet Diffusion with Wavelet U-Net & High computational cost; limited to T1 MRI; does not incorporate multi-modal data or longitudinal timepoints. \\ \hline
[39] & Custom MRI dataset (Kaggle) & CNN + DCGAN (adaptation) & Risk of overfitting due to small original dataset; no reporting of sensitivity/specificity; limited to image data. \\ \hline
[40] & ADNI2, NIFD (in-domain), NACC (external) & 125 3D U-Nets + Ensemble (MLP + SVM) & High computational cost (393 M parameters, 25.9 TFLOPs); inference time \(\sim\)1.6 s; only baseline MRI used; limited by class imbalance and absence of multimodal or longitudinal data. \\ \hline
[41] & ADNI & GAN + CNN, LSTM, Ensemble Networks & Limited real Alzheimer’s samples; reliance on synthetic augmentation; needs more external validation and data diversity. \\ \hline
[42] & ADNI (Discovery), SMC (Practice) & Modified HexaGAN (GAN + Semi-supervised + Imputation) & High model complexity; requires fine-tuning across datasets; limited to MRI and tabular inputs. \\ \hline
[43] & ADNI & Conditional DDPM + U-Net & Small dataset, imbalanced classes; specificity not reported; limited to static MRI slices; no multi-modal or longitudinal data. \\ \hline
[44] & DNI1, ADNI3, AIBL & Latent Diffusion-based SR + Siamese DenseNet & High computational cost; needs advanced infrastructure for training; diffusion SR takes longer than CNN-based methods. \\ \hline
[45] & ADNI & GAN + Transfer Learning (CNN + InceptionV3) & Class imbalance still impacts performance slightly; more detailed metrics (sensitivity/specificity) not reported. \\ \hline
[46] & ADNI & Encoder–Decoder GAN with BiGRU layers & Computational complexity, GAN training instability, underutilization of multimodal data (e.g., neuroimaging). \\ \hline
[47] & ADNI, OASIS-3, Centiloid & Latent Diffusion Model (LDM-RR) & High computational cost; trained on synthetic data; limited interpretability; real-time deployment needs optimization. \\ \hline
[48] & ADNI & U-NET-based Diffusion Model with MLP Classifier & One-way synthesis introduces variability; computational intensity; requires improvement in generalization and speed. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._A hybrid model combining Deep Super-Resolution GAN (DSR-GAN) with a CNN to classify Alzheimer's into four stages using MRI scans is presented by [PERSON] et al. (2025) [57]. The GAN enhanced image clarity, helping the CNN achieve high accuracy of 99.22% with nearly perfect precision, recall, F1-score (all \(\sim\)99%), and an AUC of 100%. The super-resolution also improved image quality (PSNR 29.30, SSIM 0.847). Despite its strong results, the model requires heavy computation and has limited scalability. Future work should expand datasets, including other imaging types like PET, and add explainability to support clinical use.