吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (2): 508-515.doi: 10.13229/j.cnki.jdxbgxb201402037

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Multiple kernel MtLSSVM and its application in lung nodule recognition

LI Yang1,2, WEN Dun-wei3, WANG Ke1, LIU Le2   

  1. 1. College of Communication Engineering, Jilin University, Changchun 130012, China;
    2. College of Computer Science and Engineering, Changchun University of Technology, Changchun 130022, China;
    3. School of Computing and Information Systems, Athabasca University, Athabasca, Alberta T9S3A3, Canada
  • Received:2013-06-08 Online:2014-02-01 Published:2014-02-01

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Abstract:

Traditional methods for lung nodule recognition need to extract the features of the Region of Interests (ROIs), which usually leads to loss of some implicit structure information. To avoid this problem, a novel Multiple Kernel Learning method based on Matrix Least Square Support Vector Machine (MKL-MatLSSVM) is proposed. This method combines the advantages of both MKL method and MatLSSVM, and supports direct matrix input, suitable for image identification. To verify the effectiveness of the proposed method, it was applied to identify lung nodules in CT images of 20 patients, where the extracted ROIs contain 80 nodules and 190 false positives. The results show that when using hybrid or Radial Basis Function (RBF) kernels in MKL-MatLSSVM, the resulting sensitivity, accuracy and specificity can be balanced, and the area under the Receiver Operating Characteristic (ROC) curve can reach 96%, better than other two previous Support Vector Machine (SVM) methods that include MatLSSVM.

Key words: information processing, image recognition, lung nodule recognition, MKL-MatLSSVM algorithm, multiple kernel learning, support vector machines

CLC Number: 

  • TP391.4

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