Alzheimer's disease detection using deep learning and machine learning: a review

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing challenges in early diagnosis and treatment. Advances in artificial intelligence (AI) have revolutionized medical image analysis, providing robust frameworks for accurate and automated AD detection. This paper reviews recent developments in deep learning (DL) and machine learning (ML) models for AD classification, like convolutional neural networks (CNNs), transfer learning, hybrid architectures, and novel attention mechanisms. Additionally, applications of AD based on AI models, datasets, preprocessing techniques, challenges, and recent studies in this field are discussed. Also, the paper provides different medical modalities, factors of increasing risk of Alzheimer, progress stages of this disease, and several metrics of assessing AI models' performance. These metrics such as accuracy, matthews correlation coefficient (MCC), F1-score, recall, precision, area under the receiver operating characteristic (ROC) curve, confusion matrix, and loss. Further, the paper presents several comparisons of different DL approaches for AD, limitations, new trends, suggestions, and future directions for this evolving field.