Introducing hybrid k-means, RLS learning algorithm in RBF network for low computational brain MRI multi-classification
محل انتشار: چهارمین کنفرانس ملی و دومین کنفرانس بین المللی پژوهش های کاربردی در مهندسی برق، مکانیک و مکاترونیک
سال انتشار: 1395
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 656
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شناسه ملی سند علمی:
ELEMECHCONF04_454
تاریخ نمایه سازی: 11 مرداد 1396
چکیده مقاله:
In this paper we study the performance of a Radial Basis Function (RBF) network with hybrid learning schemes on classification of multiple brain disease based on MR (Magnetic resonance) Image processing. The proposed method is shown to be superior to all previously presented classifiers including supporting vector machines (SVM), K-nearest neighbourhood (KNN) and simple RBF networks. Also the most efficient feature extraction and reduction methods were chosen based on computational complexity. Our aim in this paper along with the complexity reduction is the introduction of hybrid ‘k-means, RLS’ classifier to the sophisticated field of MRI classification. Also an improvement is made in this paper to the multiple classification of brain disease in 10 class scenario.
کلیدواژه ها:
نویسندگان
Golamreza Dadashnejhad
Department of electrical engineering, Mamaghan Branch, Islamic Azad University, Mamaghan, Iran
Saeid Masoumi
Department of electrical engineering, Mamaghan Branch, Islamic Azad University, Mamaghan, Iran