Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time

• Published in: Neural computing & applications Vol. 34; no. 17; pp. 14487 - 14509
• Main Authors: El-Sappagh, Shaker, Saleh, Hager, Ali, Farman, Amer, Eslam, Abuhmed, Tamer
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2022; Springer Nature B.V
• Subjects: Alzheimer's disease; Artificial Intelligence; Biomarkers; Cerebrospinal fluid; Classification; Cognitive ability; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Conversion; Data integration; Data Mining and Knowledge Discovery; Decision trees; Deep learning; Image Processing and Computer Vision; Impact prediction; Impairment; Machine learning; Medical imaging; Model accuracy; Multivariate analysis; Neuroimaging; Original Article; Probability and Statistics in Computer Science; Regression; Support vector machines; Deep learning; Alzheimer’s disease; Time-series data analysis; Alzheimer’s progression detection

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by thinking, behavioral and memory impairments. Early prediction of conversion from mild cognitive impairment (MCI) to AD is still a challenging task. No study has been able to predict the exact conversion time of MC...