Abstract

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

Highlights

  • Alzheimer’s disease (AD) is a general form of dementia correlated with the pathological amyloid depositions, structural-atrophy, and metabolic changes in the brain [1,2]

  • Classification of AD vs. mild cognitive impairment (MCI): The first experiment is intended towards the testing and validation of the proposed method for the AD vs. MCI classification

  • The best results were obtained by the Polar Cosine Transform (PCT) feature sets with the proposed SaDE-Wavelet neural networks (WNNs) are 93.74% accuracy, 86.0% sensitivity, 98.0% specificity, and 88.5 F1

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Summary

Introduction

Alzheimer’s disease (AD) is a general form of dementia correlated with the pathological amyloid depositions, structural-atrophy, and metabolic changes in the brain [1,2]. AD usually matures when the nerve cells in the brain die or their functioning becomes abnormal [3,4]. It is a major source of dementia among older people with 47 million people worldwide living with dementia in 2016 [5]. Developing countries are the most affected by this growth rate as 59% of dementia people already living there. This figure maybe around 59% by 2050 [6]. Early detection of Alzheimer’s disease can be the key to slowing, preventing, and stopping the occurrence of dementia at its early stage [7]

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