Alzheimer_s Dataset

Alzheimer’s disease, a devastating neurodegenerative disorder, has no cure, and therefore demands early diagnosis to prevent its progression. However, limited and imbalanced MRI datasets have hindered progress in AI-driven solutions. This dataset is the result of pioneering research designed to solve two major challenges in Alzheimer’s Disease (AD) research: data scarcity and severe class imbalance. By harnessing Wasserstein GANs with Gradient Penalty (WGAN-GP), we generated high-fidelity DeepFake "axial" MRI scans that match real-world data quality while ensuring balanced representation across 4 distinct disease stages. Due to this high quality and balanced nature it resulted in in an overall increase of 11.77% in Balanced Accuracy, 15% increase in Matthew's Correlation Coefficient (MCC). The study also found a 91.4% improvement in the performance on minority classes. If you're interested in training your own GANs using our code, you can understand how its done in the published research, available here:https://docs.google.com/viewer?url=https://www.ijert.org/download/thesis/2023/IJERTTH0025.pdf