Abstract
Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.
Highlights
Alzheimer’s disease (AD) is a cognitive degenerative disorder leading to dementia and is considered a mental and physical disability
The Differential Evolution-Multiclass Support Vector Machine (DE-Multi-Class Support Vector Machine (MSVM)) model is proposed to increase the performance of Alzheimer’s classification. e Alzheimer’s Disease Neuroimaging Initiative (ADNI) fMRI and Positron Emission Tomography (PET) images were used to test the performance of Alzheimer’s classification. e normalization method is applied to enhance the quality of the images
AlexNet feature extraction method and Differential Evolution (DE) feature selection are applied to select the relevant features for the classification. e MSVM model is applied with selected features and classifies Alzheimer’s images. is section provides detailed information on the results of the proposed DE-MSVM method
Summary
Alzheimer’s disease (AD) is a cognitive degenerative disorder leading to dementia and is considered a mental and physical disability. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans are common imaging techniques to analyze Alzheimer’s [2]. Mild Cognitive Impairment (MCI) is AD at its transition state, and this is necessary to classify the stages for therapeutic measures to delay the disease progression [3]. Clinical neuroimaging techniques such as MRI and PET scans are suitable for analyzing brain changes with AD progression and MCI [4]. Clinical neuroimaging techniques such as MRI and PET scans are suitable for analyzing brain changes with AD progression and MCI [4]. e structural MRI scans provide detailed information of the brain anatomical structures that can detect and measures AD of brain atrophy patterns [5]
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