Deep learning-based CAD system for Alzheimer's diagnosis using deep downsized KPLS.
Authors
Affiliations (3)
Affiliations (3)
- Department of Computer Engineering, College of Computer Science and Engineering, University of Ha'il, 2440, Ha'il, Saudi Arabia. [email protected].
- Department of Computer Engineering, College of Computer Science and Engineering, University of Ha'il, 2440, Ha'il, Saudi Arabia.
- Laboratory of Electronics and Microelectronics, Faculty of Sciences of Monastir, University of Monastir, 5019, Monastir, Tunisia.
Abstract
Alzheimer's disease (AD) is the most prevalent type of dementia. It is linked with a gradual decline in various brain functions, such as memory. Many research efforts are now directed toward non-invasive procedures for early diagnosis because early detection greatly benefits the patient care and treatment outcome. Additional to an accurate diagnosis and reduction of the rate of misdiagnosis; Computer-Aided Design (CAD) systems are built to give definitive diagnosis. This paper presents a novel CAD system to determine stages of AD. Initially, deep learning techniques are utilized to extract features from the AD brain MRIs. Then, the extracted features are reduced using a proposed feature reduction technique named Deep Downsized Kernel Partial Least Squares (DDKPLS). The proposed approach selects a reduced number of samples from the initial information matrix. The samples chosen give rise to a new data matrix further processed by KPLS to deal with the high dimensionality. The reduced feature space is finally classified using ELM. The implementation is named DDKPLS-ELM. Reference tests have been performed on the Kaggle MRI dataset, which exhibit the efficacy of the DDKPLS-based classifier; it achieves accuracy up to 95.4% and an F1 score of 95.1%.