## 4 Background Study
### Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Technique/Method** & **Model** & **Results** \\ \hline \multirow{4}{*}{[40]} & Deep Grading + & & In-domain (3-class): Accuracy: 86.0\%, \\  & Multi-layer Perceptron + & 125 3D U-Nets + Ensemble (MLP + SVM) & BACC: 84.7\%, AUC: 93.8\%, Sensitivity \\  & Multi-layer Perceptron + & 125 3D U-Nets + Ensemble (CN /AD/FTD): 89.6/83.2/81.3; \\  & SVM Ensemble (Structure & (MLP + SVM) & Out-of-domain: Accuracy: 87.1\%, BACC: 81.6\%, AUC: 91.6\%, Sensitivity \\  & Grading + Atrophy) & & 81.6\%, AUC: 91.6\%, Sensitivity \\  & & & (CN /AD/FTD): 89.6/76.9/78.4 \\ \hline \multirow{4}{*}{[41]} & GAN for synthetic MRI & & GAN results: Precision: 0.84, Recall: 0.76, \\  & Generation + Ensemble & GAN + CNN, LSTM, & F1-score: 0.80, AUC-ROC: 0.91, \\  & generation + Ensemble & Ensemble Networks & Proposed Ensemble: Precision: 0.85, \\  & deep learning classifiers & & Recall: 0.79, F1-score: 0.82, AUC-ROC: 0.93 \\ \hline \multirow{4}{*}{[42]} & & ADNI: AUROC 0.8609, Accuracy 0.8244, \\  & Modified HexaGAN (Deep + Semi-supervised + & F1-score 0.7596, Sensitivity 0.8415, \\  & Generative Framework) & + Semi-supervised + & Specificity 0.8178; SMC: AUROC 0.9143, \\  & Imputation) & & Accuracy 0.8528, Sensitivity 0.9667, \\  & & & Specificity 0.8286. \\ \hline \multirow{4}{*}{[43]} & Conditional Diffusion & & Best result (Combine 900): Accuracy: \\  & Model for Data & Conditional DDPM + & 74.73\%, Precision: 77.28\%, Recall \\  & Augmentation & U-Net & (Sensitivity): 66.52\%, F1-score: 0.6968, \\  & & AUC: 0.8590; Specificity: not reported \\ \hline \multirow{4}{*}{[44]} & Latent Diffusion Model & & \\  & (d3T*) for MRI & & AD classification: Accuracy 92.3\%, \\  & super-resolution + & Latent Diffusion-based SR & AUROC 93.1\%, F1-score 91.9\%; \\  & DenseNet Siamese & + Siamese DenseNet & Significant improvement over 1.5 T and \\  & Network for AD/MCI/NC & & c3T*; Comparable to real 3 T MRI \\  & classification & & \\ \hline \multirow{4}{*}{[45]} & GAN-based data & & \\  & augmentation + hybrid & GAN + Transfer Learning & Accuracy: 90.91\%; metrics like precision, \\  & CNN-InceptionV3 model & (CNN + InceptionV3) & recall, and F1-score also reported high \\  & for multiclass AD & & performance \\  & classification & & \\ \hline \multirow{4}{*}{[46]} & DeepCGAN (GAN + & Encoder–Decoder GAN & Accuracy: 97.32\%, Recall (Sensitivity): 95.43\%, Precision: 95.31\%, F1-Score: \\  & BiGRU with Wasserstein Loss) & with BiGRU layers & 95.61\%, AUC: 99.51\% \\ \hline \multirow{4}{*}{[47]} & Latent Diffusion Model for & & Recovery coefficient: 0.96; Longitudinal \\  & Resolution Recovery & Latent Diffusion Model & \(p\)-value: 1.3 \(\times\) 10\({}^{-10}\); Cross-tracer \\  & (LDM-RR) & (LDM-RR) & correlation: r = 0.9411; Harmonization \\  & & & \(p\) = 0.0421 \\ \hline \multirow{4}{*}{[48]} & Diffusion-based multi-view & U-NET-based Diffusion & Accuracy: 82.19\%, SSIM: 0.9380, PSNR: \\  & learning (one-way and & Model with MLP Classifier & 26.47, Sensitivity: 95.19\%, Specificity: 92.98\%, Recall: 82.19\% \\  & two-way synthesis) & & 92.98\%, Recall: 82.19\% \\ \hline \multirow{4}{*}{[49]} & Conditional DDPM and & & AUC: 0.870, F1-score: 0.760, Sensitivity: \\  & LDM with counterfactual generation and & LDM + 3D DenseNet121 & 0.889, Specificity: 0.837 (ADNI test set \\  & DenseNet121 classifier & & after fine-tuning) \\ \hline \multirow{4}{*}{[50]} & GANs, VAEs, Diffusion & & \\  & (DDIM) models for MRI & DDIM (Diffusion Model) + & Accuracy: 80.84\%, Precision: 86.06\%, \\  & generation + & DenseNet & Recall: 78.14\%, F1-Score: 80.98\% \\ \cline{1-1}  & DenseNet/ResNet & & (Alzheimer’s, DenseNet + DDIM) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._[PERSON] et al. (2023) [42] introduced a modified HexaGAN model to predict amyloid positivity in cognitively normal individuals using MRI, cognitive scores, and demographic data. Built to handle missing data and imbalanced classes, the model achieved strong performance with AUROCs of 0.86 (ADNI) and 0.91 (clinical dataset).