DocumentCode :
237985
Title :
Detection of Alzheimer disease in brain images using PSO and Decision Tree Approach
Author :
Sweety, M. Evanchalin ; Jiji, G. Wiselin
Author_Institution :
Comput. Sci. & Eng., Dr. Sivanthi Aditanar Coll. of Eng., Tiruchendur, India
fYear :
2014
fDate :
8-10 May 2014
Firstpage :
1305
Lastpage :
1309
Abstract :
Alzheimer´s disease (AD) is a disease that attacks the brain which worsens as it progresses and it eventually lead to the death. This paper is based on the proposed technique Particle swarm optimization (PSO) for feature reduction and Decision Tree Classifier for classification. Earlier detection of AD is carried out in 3 phases. In the first phase, features such as eigen vectors, eigen brain, mean, variance, skewness, kurtosis, standard deviation, area, perimeter, eccentricity are extracted from MRI Images. In the second phase, feature reduction is carried out by Particle swarm optimization(PSO) and in third phase, Decision Tree Classifier is used to detect whether the brain image is affected by the Alzheimer disease or not. The proposed work is also compared with earlier works.
Keywords :
biomedical MRI; brain; decision trees; diseases; feature extraction; image classification; medical image processing; neurophysiology; particle swarm optimisation; AD detection; Alzheimer disease detection; MRI images; PSO; brain image; decision tree classifier; feature extraction; feature reduction; particle swarm optimization; Alzheimer´s disease; Brain; Decision trees; Feature extraction; Particle swarm optimization; Support vector machines; Alzheimer disease; Particle Swarm Optimization; Support Vector Machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on
Conference_Location :
Ramanathapuram
Print_ISBN :
978-1-4799-3913-8
Type :
conf
DOI :
10.1109/ICACCCT.2014.7019310
Filename :
7019310
Link To Document :
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