Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification
المؤلفون المشاركون
Rajesh Sharma, R.
Marikkannu, P.
المصدر
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-10-04
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الطب البشري
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper.
Classification of medical images is substantial in both clinical and research areas.
Magnetic resonanceimaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more.
The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant.
The design approach was initially preprocessed using 3D Gaussian filter.
Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix.
The optimal features are selected using the proposed refined gravitational searchalgorithm (RGSA).
Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach.
The preliminary evaluation of the system is performed using 320 real-time brain MRI images.
The system is trained and tested by using a leave-one-case-out method.
The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002).
The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Rajesh Sharma, R.& Marikkannu, P.. 2015. Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification. The Scientific World Journal،Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1078540
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Rajesh Sharma, R.& Marikkannu, P.. Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification. The Scientific World Journal No. 2015 (2015), pp.1-14.
https://search.emarefa.net/detail/BIM-1078540
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Rajesh Sharma, R.& Marikkannu, P.. Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification. The Scientific World Journal. 2015. Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1078540
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1078540
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