Non-white matter tissue extraction and deep convolutional neural network for Alzheimer’s disease detection

• Published in: Soft computing (Berlin, Germany) Vol. 22; no. 20; pp. 6825 - 6833
• Main Authors: Vu, Tien-Duong, Ho, Ngoc-Huynh, Yang, Hyung-Jeong, Kim, Jahae, Song, Ho-Chun
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2018; Springer Nature B.V
• Subjects: Anniversaries; Artificial Intelligence; Artificial neural networks; Brain research; Classification; Cognitive ability; Computational Intelligence; Control; Deep learning; Dementia; Disease; Engineering; Focus; Machine learning; Magnetic resonance imaging; Mathematical Logic and Foundations; Mechatronics; Medical imaging; Memory; Methods; Neural networks; Positron emission; Robotics; Support vector machines; Tomography; Alzheimer’s disease diagnostic; HiLCAE; 3D-VGG16; MRI; Non-WM PET

In this paper, we introduce an automatic and robust method to detect and identify Alzheimer’s disease (AD) using the magnetic resonance imaging (MRI) and positron emission tomography (PET) images. AD research as utilized with clinical and computer aid diagnostic tools has been strongly developed in...