DenseNet Framework for MRI-Based Alzheimer's Disease Classification with Feature Enhancement

A study of DenseNet-based deep learning architecture designed to enhance multi-class diagnosis of Alzheimer's disease using MRI analysis. The method includes a comprehensive preprocessing pipeline and a feature enhancement technique that utilizes copula-based distribution alignment and high-fidelity synthetic augmentation to address class imbalance and strengthen minor structural representations. The tight connection of DenseNet optimizes feature reuse, allowing the model to detect subtle neuroanatomical alterations associated with cognitive decline. Quantitative metrics such as FID, SSIM, PSNR, and sharpness difference guarantee the trustworthiness of improved MRI samples used in training. The suggested model, using the Kaggle Alzheimer's Dataset, exhibits robust diagnostic proficiency throughout four phases, attaining an accuracy of 98.44%, precision of 96.97%, recall of 96.88%, and$\mathbf{F 1}$-score of 96.92. These findings validate the framework's efficacy in generating reliable and clinically interpretable predictions. The system integrates dense feature propagation with distribution-aware augmentation, providing a robust and scalable method for early Alzheimer's identification and enhancing subsequent clinical decision-making.