Feature abstraction for early detection of multi-type of dementia with sparse auto-encoder

Abstract

With millions of people suffering from dementia worldwide, the global prevalence of dementia has a significant impact on the patients' lives, their caregivers' physical and emotional states, and the global economy. Early diagnosis of dementia helps in finding suitable therapies that reduce or even prevent further deterioration of patients' cognitive abilities. MRI scans are shown to be the most effective strategy to enable early diagnosis of dementia. Nevertheless, the early diagnosis of dementia is a challenging task due to the high dimensionality of MRI scans that may degrade the effectiveness of machine learning models. Feature abstraction is a part of dimensionality reduction process need by researchers to represent input data in its simplest form that results in a more-robust system model. It is a technique that collects relevant features and ignores irrelevant or redundant ones from data without loss of much key information. This paper proposes a novel feature abstraction method using sparse autoencoder (SAE) to reduce the dimensionality of and extract key features from MRI neuroimages. These features and that obtained from the popular PCA approach are then used to train a Linear Discriminant Analysis (LDA) and Logistic Regression classifiers in order to compare their prediction accuracy. The experimental results show that the proposed approach yields higher classification accuracy compared to that using the PCA by 8 percent of classification accuracy. The experimental results show that the use of features learned by SAE in early diagnosis of multi-type dementia provides better classification performance than the use of raw image pixel intensities for diagnosing dementia.