Review of Brain Imaging Techniques, Feature Extraction and Classification Algorithms to Identify Alzheimer’s Disease

Ahila Arumugam Annakutty, A. Aponso
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引用次数: 5

Abstract

Alzheimer’s disease is one of the most increasing neurodegenerative disorder which mainly affects the memory, brain functioning and thinking of elders. Since the cure for this disease is yet to be found, it’s vital to diagnose Alzheimer’s disease in the early stages and to delay the progress of the disease as much as possible. There have been many researches conducted to diagnose Alzheimer’s disease using different brain imaging techniques and computational methods. The main aim of this paper is to review brain imaging techniques, preprocessing algorithms and classification algorithms to identify the most suitable approach to diagnose Alzheimer’s disease. Specifically this paper consists of following sections: (i) A brief description of the disease and the case; (ii) Review of brain imaging techniques (EEG, MEG, MRI and FMRI); (iii) Review and comparison of preprocessing algorithms (FFT, Wavelet transform and TFD); (iv) Review and comparison of classification algorithms (SVM, decision tree, neural network and random forest).1
脑成像技术、特征提取和分类算法在阿尔茨海默病诊断中的应用综述
阿尔茨海默病是一种发病率最高的神经退行性疾病,主要影响老年人的记忆、脑功能和思维。由于这种疾病的治疗方法尚未找到,因此在早期诊断阿尔茨海默病并尽可能延缓疾病的发展至关重要。已有许多研究利用不同的脑成像技术和计算方法来诊断阿尔茨海默病。本文的主要目的是回顾脑成像技术,预处理算法和分类算法,以确定最合适的方法来诊断阿尔茨海默病。具体而言,本文由以下部分组成:(i)对疾病和病例的简要描述;审查脑成像技术(脑电图、MEG、核磁共振和功能核磁共振);(iii)预处理算法(FFT、小波变换和TFD)的回顾和比较;(iv)分类算法(支持向量机、决策树、神经网络、随机森林)的回顾与比较
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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