Deep learning-based CAD system for Alzheimer's diagnosis using deep downsized KPLS

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%.

Keywords: AD; Alzheimer’s disease; CAD system; DDKPLS; Deep learning; Feature reduction; KPLS.

Fig. 1

Block diagram for proposed work based on DDKPLS for dimensionality reduction.

Fig. 2

Flowchart depiction of the proposed analysis framework.

Fig. 3

Block diagram of DDKPLS.

Fig. 4

Block diagram of DDKPLS for dimensionality reduction.

Fig. 5

Flowchart of DKPLS.

Fig. 6

Sample images of the four AD classes: (a) MID (b) MOD (c) ND (d) VMD.

Fig. 7

Confusion matrix of DDKPLS-ELM.