Round Randomized Learning Vector Quantization for Brain Tumor Imaging
المؤلفون المشاركون
Sheikh Abdullah, Siti Norul Huda
Bohani, Farah Aqilah
Nayef, Baher H.
Sahran, Shahnorbanun
Al Akash, Omar
Iqbal Hussain, Rizuana
Ismail, Fuad
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-19، 19ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-07-18
دولة النشر
مصر
عدد الصفحات
19
التخصصات الرئيسية
الملخص EN
Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task.
Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential.
The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs.
The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function.
In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function.
Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution.
Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved.
This multiresampling is executed by using random selection via preclassification.
The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets.
Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sheikh Abdullah, Siti Norul Huda& Bohani, Farah Aqilah& Nayef, Baher H.& Sahran, Shahnorbanun& Al Akash, Omar& Iqbal Hussain, Rizuana…[et al.]. 2016. Round Randomized Learning Vector Quantization for Brain Tumor Imaging. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-19.
https://search.emarefa.net/detail/BIM-1100213
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sheikh Abdullah, Siti Norul Huda…[et al.]. Round Randomized Learning Vector Quantization for Brain Tumor Imaging. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-19.
https://search.emarefa.net/detail/BIM-1100213
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sheikh Abdullah, Siti Norul Huda& Bohani, Farah Aqilah& Nayef, Baher H.& Sahran, Shahnorbanun& Al Akash, Omar& Iqbal Hussain, Rizuana…[et al.]. Round Randomized Learning Vector Quantization for Brain Tumor Imaging. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-19.
https://search.emarefa.net/detail/BIM-1100213
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1100213
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر