PARTICLE SWARM OPTIMIZED FEATURE SELECTION FOR ALZHEIMER CLASSIFICATION

被引：0

作者：

Sountharrajan, S. ^{[1
]}

Thangaraj, P. ^{[1
]}

Suganya, E. ^{[1
]}

机构：

[1] Bannari Amman Inst Technol, Sathyamangalam, Tamil Nadu, India

来源：

IIOAB JOURNAL | 2016年 / 7卷 / 09期

关键词：

Alzheimer's disease (AD); Magnetic Resonance Imaging (MRI); Particle Swarm Optimization (PSO);

D O I：

暂无

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Alzheimer's disease (AD) refers to a neuro-degenerative chaos that is a general kind of dementia which leads to memory loss, and lack of cognitive functioning and so on. Magnetic Resonance Imaging (MRI) is popularly utilized for human body imagings. MRI is a fundamentally non-invasive method giving high degree clarity on the soft tissue inside the brain better than conventional Computed Tomography (CT), ultrasound, Positron Emission Tomography (PET), etc. SVM is a kind of ANN (artificial neural network) which has got training from supervised learning methods and had showed the benefits of decreasing the training-testing error and hence producing greater recognition precision. This paper investigates empirically PSO's (Particle Swarm Optimization's) effectiveness towards selection of features.

引用

页码：787 / 794

页数：8

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