An Enhanced Deep Learning Approach for Classification of Alzheimer's Disease Stages Using MRI

Alzheimer's disease (AD) is a degenerative brain illness that primarily affects older adults and causes significant cognitive impairment. Early and accurate diagnosis is crucial for effective management and intervention. The purpose of this study was to use MRI to categorize AD into four different stages: non-demented, very mildly demented, mildly demented, and moderately demented. A customized MobileNetV2 deep learning model was created using the ADNI Dataset, and compared with CNN and VGG19 models. To rectify class imbalance and improve image quality, sophisticated preprocessing methods such as data augmentation, Gaussian blurring, and grayscale conversion, were used. The proposed MobileNetV2 model obtained a remarkable 98% accuracy and strong F1 scores in each category using dense and dropout layers to reduce overfitting. The proposed model demonstrates strong potential for reliable AD classification, and future research will focus on extending its applicability to more extensive and varied datasets.