Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE
المؤلفون المشاركون
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-04-06
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
In lung cancer computer-aided detection/diagnosis (CAD) systems, classification of regions of interest (ROI) is often used to detect/diagnose lung nodule accurately.
However, problems of unbalanced datasets often have detrimental effects on the performance of classification.
In this paper, both minority and majority classes are resampled to increase the generalization ability.
We propose a novel SVM classifier combined with random undersampling (RU) and SMOTE for lung nodule recognition.
The combinations of the two resampling methods not only achieve a balanced training samples but also remove noise and duplicate information in the training sample and retain useful information to improve the effective data utilization, hence improving performance of SVM algorithm for pulmonary nodules classification under the unbalanced data.
Eight features including 2D and 3D features are extracted for training and classification.
Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sui, Yuan& Wei, Ying& Zhao, Dazhe. 2015. Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE. Computational and Mathematical Methods in Medicine،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1057877
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sui, Yuan…[et al.]. Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE. Computational and Mathematical Methods in Medicine No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1057877
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sui, Yuan& Wei, Ying& Zhao, Dazhe. Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE. Computational and Mathematical Methods in Medicine. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1057877
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1057877
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