Classification of Alzheimer's Disease Using a Hybrid Technique Integration Between CNN and Optuna Optimization

Abstract

Alzheimer's Disease (AD) is considered one of the most prevalent neurological disorders, primarily affecting elderly people and adversely impacting their brain functions. This disease is characterized by the gradual deterioration of cognitive functions, particularly memory, leading to varying information loss levels, typically classified within the framework of dementia. In this context, Artificial Intelligence (AI) methods, especially Convolutional Neural Networks (CNN) and Optuna optimization, have emerged as effective technique to provide efficient approach (or model) for classifying AD levels. The proposed approach is called the Optuna-CNN (O-CNN). It has the ability to identify the optimal CNN architecture by employing the optimization of Optuna. It is designed to classify AD into four levels (or stages): Mild Dementia (MD), Moderate Dementia (MoD), No Dementia (ND) and Very Mild Dementia (VMD). Two datasets are utilized here: the Best Alzheimer's MRI Dataset (BA-MRI) and the Alzheimer's MRI Dataset (AD-MRI), both are obtained from Kaggle platform. Following extensive training and implementations of the O-CNN method, high classification accuracies 94.79% for the AD-MRI dataset and 99.18% for the BA-MRI dataset are achieved.