## 4 Background Study
### Literature Review
Using a Deep Convolutional GAN (DCGAN), they trained the model on real PET scans from the ADNI dataset to produce high-quality synthetic images. The generated images showed strong visual and statistical similarity to real ones, achieving a PSNR of 32.83 and SSIM of 77.48. When used to train a CNN classifier, these synthetic images improved classification accuracy by 10%, reaching 71.45%. Although promising, the method's limitations include the need to train separate models for each class and the reliance on 2D image slices. [PERSON] et al. [27] introduced a Bidirectional GAN to generate realistic brain PET images from MR scans, aiming to preserve individual brain structure differences. Their model uses a ResU-Net generator and ResNet-based encoder, combining adversarial, pixel-wise, and perceptual losses for better image quality. Tested on 680 subjects from the ADNI dataset, it achieved PSNR of 27.36, SSIM of 0.88, and improved AD vs. CN classification accuracy to 87.82%. While effective, the method could benefit from improved handling of latent vector injection for finer image details. To address the issue of class imbalance in Alzheimer's datasets, [PERSON] et al. [28] developed a DCGAN-based approach that generates synthetic PET images for underrepresented AD cases. These generated images were added to the training set to balance the data, improving the performance of a DenseNet-based classifier. As a result, classification accuracy increased from 67% to 74%. The synthetic images were evaluated using MMD (1.78) and SSIM (0.53), showing good diversity and realism. While effective, the method uses 2D generation and manual filtering. Future work could involve 3D GANs and smarter sample selection for better scalability. [PERSON] et al. [29] presented a 3D Multi-Information GAN (mi-GAN) framework that predicts Alzheimer's Disease progression by generating future 3D brain MRI scans conditioned on baseline scans and patient metadata (age, gender, education, and APOE status). The model uses a 3D U-Net generator and DenseNet-based multi-class classifier optimized with focal loss. Evaluated on the ADNI dataset, mi-GAN achieved a high SSIM of 0.943, and the classifier reached 76.67% accuracy, with a pMCI vs. sMCI accuracy of 78.45%,outperforming previous cGAN and cross-entropy-based models. Limitations include less accuracy in gray matter prediction and short-term progression. Future work could benefit from improved gray matter modeling and broader clinical feature integration. A novel unsupervised anomaly detection framework MADGAN was proposed by [PERSON] et al. (2021) [30], that reconstructs multiple adjacent MRI slices to detect subtle brain anomalies like Alzheimer's disease (AD) and brain metastases. The model uses a self-attention GAN architecture trained solely on healthy MRI slices to predict the next 3 slices from the previous 3, comparing reconstruction loss to detect anomalies. The method achieved promising AUCs for AD detection: 0.727 for early-stage (MCI) and 0.894 for late-stage AD, and 0.921 for brain metastases, highlighting its effectiveness across different disease types and stages. The key advantage of MADGAN lies in leveraging healthy data only, mimicking a physician's diagnostic intuition. However, the reconstruction is less stable in texture consistency, especially for contrast-enhanced (T1c) images, and detection performance can vary with SA module configurations. Future work could involve enhancing attention mechanisms and exploring additional loss functions to improve generalizability and lesion localization.