## 4 Background Study
### Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Modality** & **Total Number of Participants** \\ \hline
[21] & \
\begin{tabular}{c} ADNI-like MRI data (T1w), \\ Diffusion MRI of healthy subjects \\ \end{tabular} & Structural MRI &
\begin{tabular}{c} 18 AD, 18 bvFTD, 19 control: 14 \\ healthy for connectome \\ \end{tabular} \\ \hline
[23] & ADNI-1 and ADNI-2 & MRI, PET & ADNI-1: 821; ADNI-2: 636 \\ \hline
[24] & OASIS-3 & MRI &
\begin{tabular}{c} Training: 408 subjects; Test: 113 \\ healthy, 99 (CDR 0.5), 61 (CDR 1), \\ 4 (CDR 2) \\ \end{tabular} \\ \hline
[25] & ADNI & MRI, PET & 1033 (722 train, 104 val, 207 test) \\ \hline
[26] & ADNI & PET & 411 PET scans (98 AD, 105 NC, 208 MCI) \\ \hline
[27] & ADNI & MRI, PET & 680 subjects \\ \hline
[28] & ADNI-1, ADNI-2 & MRI, PET &
\begin{tabular}{c} ADNI-1 (PET AD only), ADNI-2 \\ (100 NC, 20 AD, 80 AD MRI-only) \\ \end{tabular} \\ \hline
[29] & ADNI-GO, ADNI-2, OASIS & 3D MRI &
\begin{tabular}{c} 210 (mi-GAN), 603 (classifier), 48 \\ (validation) \\ \end{tabular} \\ \hline
[30] & OASIS-3, Internal dataset & T1, T1c MRI & 408 (T1), 135 (T1c healthy) \\ \hline
[31] & ADNI, AIBL, NACC & MRI (1.5-T and 3-T) &
\begin{tabular}{c} ADNI: 151 (training), AIBL: 107, \\ NACC: 565 \\ \end{tabular} \\ \hline
[32] & ADNI-1, ADNI-2 & MRI + PET & ADNI-1: 821; ADNI-2: 534 \\ \hline
[33] & ADNI-1 & T1 MRI & 833 (221 AD, 297 MCI, 315 NC) \\ \hline
[34] & ADNI (268 subjects) & rs-fMRI + DTI & 268 \\ \hline
[35] & \
\begin{tabular}{c} ADNI (13,500 3 DRI images \\ after augmentation) \\ \end{tabular} & 3D Structural MRI &
\begin{tabular}{c} 138 (original), 13,500 (augmented \\ scans) \\ \end{tabular} \\ \hline
[36] &
\begin{tabular}{c} ADNI (1732 scan-pairs, 873 \\ subjects) \\ \end{tabular} & MRI \(\rightarrow\) Synthesized PET & 873 \\ \hline
[37] & ADNI & T1-weighted MRI & 632 participants \\ \hline
[38] & ADNI2 & T1-weighted MRI &
\begin{tabular}{c} 169 participants, 27,600 image \\ pairs \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of medical imaging datasets and their modalities used in studies. [PERSON] et al. [31] explored GANs to enhance 1.5 T MRI scans into 3 T-like images (3-T*) for Alzheimer's classification using an FCN. These enhanced images not only looked better based on quality scores (BRISQUE, NIQE), but also helped a deep learning model improve its AD prediction accuracy boosting AUC from 0.907 to 0.932. However, the study was limited by a small sample size and did not include MCI cases. Expanding the dataset and refining the models could make this approach even more effective in future work. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Modality** & **Total Number of Participants** \\ \hline
[39] & Custom MRI dataset (Kaggle) & T1-weighted Brain MRI & 6400 images (approx.) \\ \hline
[40] & ADNI2, NIFD (in-domain), \(  
A hybrid deep learning model combining TPA-GAN and PT-DCN to generate missing PET scans from MRI and classify Alzheimer's disease is developed by [PERSON] et al. (2022) [32]. Tested on ADNI-1/2, it achieved 90.7% accuracy (AUC 0.95) for AD vs. CN and 85.2% accuracy (AUC 0.89) for pMCI vs. sMCI. Despite strong results, its reliance on PET-MRI pairs and dataset-specific training limits generalizability. Future work could address this with domain adaptation. [PERSON] et al. [33] proposed THS-GAN, a semi-supervised generative adversarial network that introduces tensor-train decomposition and high-order pooling (GSP block) for Alzheimer's disease (AD) and mild cognitive impairment (MCI) classification from T1-weighted MRI scans. Their framework improves over traditional GANs by using a three-player cooperative game--generator, discriminator, and classifier--while tensor-train layers reduce parameters and preserve brain structural information. Global second-order pooling enhances discriminative feature representation.