Artificial intelligence techniques for classification of Alzheimer's disease using neuroimaging data: a review

Alzheimer's disease (AD) is a gradually advancing brain disorder marked by memory impairment. The incurable, progressive nature of the disease leads to the dementia stage. Treatment is effective in the early stage, and it can be controlled but not cured. Artificial Intelligence ( AI) learning models are used in medical science to detect and classify diseases into specific categories. Features are extracted from medical images and trained using AI learning models to perform an accurate diagnosis of AD. Recent advancements in machine learning (ML) and deep learning (DL) models have demonstrated significant potential in identifying AD across various data modalities, including neuroimaging, genetic information, and clinical assessments. This study focuses on the application of advanced ML and DL techniques in the identification and classification of AD, including regression models, decision trees, random forests, support vector machines (SVMs), k-nearest neighbors (KNNs), ensemble models, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Each model is analyzed