Detection of Alzheimer’s disease using pre-trained deep learning models through transfer learning: a review

• Published in: The Artificial intelligence review 2024-09, Vol.57 (10), p.275, Article 275
• Main Authors: Heenaye-Mamode Khan, Maleika, Reesaul, Pushtika, Auzine, Muhammad Muzzammil, Taylor, Amelia
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Artificial Intelligence; Artificial neural networks; Automation; Classification; Computer Science; Deep learning; Feature extraction; Image processing; Machine learning; Medical imaging; Medical research; Neural networks; Positron emission
• ISSN: 1573-7462; 0269-2821; 1573-7462
• DOI: 10.1007/s10462-024-10914-z

Due to the progress in image processing and Artificial Intelligence (AI), it is now possible to develop automated tool for the early detection and diagnosis of Alzheimer’s Disease (AD). Handcrafted techniques developed so far, lack generality, leading to the development of deep learning (DL) techniques, which can extract more relevant features. To cater for the limited labelled datasets and requirement in terms of high computational power, transfer learning models can be adopted as a baseline. In recent years, considerable research efforts have been devoted to developing machine learning-based techniques for AD detection and classification using medical imaging data. This survey paper comprehensively reviews the existing literature on various methodologies and approaches employed for AD detection and classification, with a focus on neuroimaging techniques such as structural MRI, PET, and fMRI. The main objective of this survey is to analyse the different transfer learning models that can be used for the deployment of deep convolution neural network for AD detection and classification. The phases involved in the development namely image capture, pre-processing, feature extraction and selection are also discussed in the view of shedding light on the different phases and challenges that need to be addressed. The research perspectives may provide research directions on the development of automated applications for AD detection and classification.