Alzheimer Diseases Dataset
Description
The Alzheimer’s Disease Dataset was used in this study to develop and evaluate the proposed Alzheimer’s disease classification framework. The dataset consists of brain imaging samples belonging to two distinct diagnostic categories: Alzheimer’s Disease (AD) and Control (CN). The AD class contains subjects diagnosed with Alzheimer’s disease, whereas the Control class represents cognitively normal individuals without a diagnosed neurodegenerative disorder. The dataset was organized into two classes, namely AD and Control, and each sample was assigned a corresponding class label. Prior to model training, the images were subjected to appropriate preprocessing operations, including image resizing, normalization, and, where applicable, data augmentation. These preprocessing steps were performed to ensure a consistent input format and to improve the robustness of the classification models. The dataset was subsequently divided into training(22284) and testing(5112) subsets while maintaining the distribution of the two classes. The training set was used for model optimization, the validation set for hyperparameter tuning and model selection, and the independent test set for final performance evaluation.