title: Construction of classifier based on MPCA and QSA and its application on classification of pancreatic diseases.
abstract: A novel method is proposed to establish the classifier which can classify the pancreatic images into normal or abnormal. Firstly, the brightness feature is used to construct high-order tensors, then using multilinear principal component analysis (MPCA) extracts the eigentensors, and finally, the classifier is constructed based on support vector machine (SVM) and the classifier parameters are optimized with quantum simulated annealing algorithm (QSA). In order to verify the effectiveness of the proposed algorithm, the normal SVM method has been chosen as comparing algorithm. The experimental results show that the proposed method can effectively extract the eigenfeatures and improve the classification accuracy of pancreatic images.
authors.affiliation: Software College, Northeastern University, Shenyang 110819, China. hyjiang@mail.neu.edu.cn
authors.forename: Huiyan
authors.initials: H
authors.lastname: Jiang
authors.forename: Di
authors.initials: D
authors.lastname: Zhao
authors.forename: Tianjiao
authors.initials: T
authors.lastname: Feng
authors.forename: Shiyang
authors.initials: S
authors.lastname: Liao
authors.forename: Yenwei
authors.initials: Y
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data*; D006801:Humans; D010182:Pancreatic Diseases / Q000145:classification* / Q000175:diagnosis* / Q000000981:diagnostic imaging; D010190:Pancreatic Neoplasms / Q000145:classification / Q000175:diagnosis / Q000000981:diagnostic imaging; D025341:Principal Component Analysis; D011789:Quantum Theory; D011857:Radiographic Image Interpretation, Computer-Assisted; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data