%0 Journal Article %1 noauthororeditor %A Sivapriya, T.R. %D 2011 %J International Journal of Computer Science, Engineering and Information Technology (IJCSEIT) %K BPN Classification MRI PCA PSO SVM Wavelet %N 5 %P 63-76 %R 10.5121/ijcseit.2011.1506 %T AN EFFICIENT WAVELET BASED FEATURE REDUCTION AND CLASSIFICATION TECHNIQUE FOR THE DIAGNOSIS OF DEMENTIA %U http://airccse.org/journal/ijcseit/papers/1211ijcseit06.pdf %V 1 %X This research paper proposes an improved feature reduction and classification technique to identify mild and severe dementia from brain MRI data. The manual interpretation of changes in brain volume based on visual examination by radiologist or a physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the need for classification of brain MRI after identifying abnormal MRI volume, for the diagnosis of dementia. In this research work, advanced classification techniques using Support Vector Machines based on Particle Swarm Optimisation and Genetic algorithm are compared. Feature reduction by wavelets and PCA are analysed. From this analysis, it is observed that the proposed classification of SVM based PSO is found to be efficient than SVM trained with GA and wavelet based feature reduction technique yields better results than PCA.

@article{noauthororeditor, abstract = {This research paper proposes an improved feature reduction and classification technique to identify mild and severe dementia from brain MRI data. The manual interpretation of changes in brain volume based on visual examination by radiologist or a physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the need for classification of brain MRI after identifying abnormal MRI volume, for the diagnosis of dementia. In this research work, advanced classification techniques using Support Vector Machines based on Particle Swarm Optimisation and Genetic algorithm are compared. Feature reduction by wavelets and PCA are analysed. From this analysis, it is observed that the proposed classification of SVM based PSO is found to be efficient than SVM trained with GA and wavelet based feature reduction technique yields better results than PCA. }, added-at = {2018-10-04T12:25:31.000+0200}, author = {Sivapriya, T.R.}, biburl = {https://www.bibsonomy.org/bibtex/2962495e3954e6586f43d587a8ca013f4/ijcseit}, doi = {10.5121/ijcseit.2011.1506}, interhash = {4ee777936ab04878c85161a08619bae2}, intrahash = {962495e3954e6586f43d587a8ca013f4}, issn = {2231-3117 [Online] ; 2231-3605 [Print]}, journal = {International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)}, keywords = {BPN Classification MRI PCA PSO SVM Wavelet}, language = {English}, month = dec, number = 5, pages = {63-76}, timestamp = {2019-08-29T08:12:18.000+0200}, title = {AN EFFICIENT WAVELET BASED FEATURE REDUCTION AND CLASSIFICATION TECHNIQUE FOR THE DIAGNOSIS OF DEMENTIA}, url = {http://airccse.org/journal/ijcseit/papers/1211ijcseit06.pdf}, volume = 1, year = 2011 }