Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO
المؤلفون المشاركون
Zhu, Zhichuan
Li, Yang
Hou, Alin
Zhao, Qingdong
Liu, Liwei
Zhang, Lijuan
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-04-29
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
Pulmonary nodule recognition is the core module of lung CAD.
The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein.
Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid.
In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly.
In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition.
The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect.
Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence.
Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm.
In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight.
Besides, a better nonlinear inertial weight is verified.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Yang& Zhu, Zhichuan& Hou, Alin& Zhao, Qingdong& Liu, Liwei& Zhang, Lijuan. 2018. Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1131783
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Yang…[et al.]. Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1131783
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Yang& Zhu, Zhichuan& Hou, Alin& Zhao, Qingdong& Liu, Liwei& Zhang, Lijuan. Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1131783
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1131783
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