Multiple kernel MtLSSVM and its application in lung nodule recognition

Yang Li, Dun Wei Wen, Ke Wang, Le Liu

Research output: Contribution to journalJournal Articlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)508-515
Number of pages8
JournalJilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
Issue number2
Publication statusPublished - Mar. 2014


  • Image recognition
  • Information processing
  • Lung nodule recognition
  • MKL-MatLSSVM algorithm
  • Multiple kernel learning
  • Support vector machines


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