Pehlivanoǧlu, Meltem Kurt; Varol, Onur; Aldemir, Osman; Uncu, Ata Emir; Duru, Nevcihan; Adali, E.

The classification of dementia levels in Alzheimer's disease is divided based on various characteristics of the disease. In this study, for the purpose of classifying dementia levels in Alzheimer's disease using MRI images, various Convolutional Neural Network (CNN)-based deep learning models, as well as machine learning and Artificial Neural Network (ANN) models, were trained. Various image manipulation and augmentation techniques were applied to create new datasets, and model performances were observed on these datasets. As a different approach, numerical features were extracted from MRI images, and models were built using these extracted features. Fusion hybrid models were produced by combining the most successful models trained with the images and their numerical representations. Experimental results have proven that hybrid models outperform other models in terms of performance in the classification of Alzheimer's disease.

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