title: Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines.
abstract: BACKGROUND
  This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images.
  
  
  METHODS
  T1-weighted (T1W) and T2-weighted (T2W) images of the 15 boys with DMD and 15 normal controls were obtained. Textural features of the images were extracted and wavelet decomposed, and then, principal features were selected. Scale transform was then performed for MRI images. Afterward, SVM-based classifiers of MRI images were analyzed based on the radical basis function and decomposition levels. The cost (C) parameter and kernel parameter [Formula: see text] were used for classification. Then, the optimal SVM-based classifier, expressed as [Formula: see text]), was identified by performance evaluation (sensitivity, specificity and accuracy).
  
  
  RESULTS
  Eight of 12 textural features were selected as principal features (eigenvalues [Formula: see text]). The 16 SVM-based classifiers were obtained using combination of (C, [Formula: see text]), and those with lower C and [Formula: see text] values showed higher performances, especially classifier of [Formula: see text]). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of [Formula: see text]) at level 2 decomposition showed the highest performance of all, and its overall correct sensitivity, specificity, and accuracy reached 96.9, 97.3, and 97.1 %, respectively.
  
  
  CONCLUSIONS
  The T1W images in SVM-based classifier [Formula: see text] at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.
authors.affiliation: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.|Lab of Big Data Analyses and Process, Information Science and Technology, Shanghai Sanda University, 2727 Jinhai Road, Pudong New District, Shanghai, 201209, People's Republic of China.
authors.forename: Ming-Huan
authors.initials: MH
authors.lastname: Zhang
authors.affiliation: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.
authors.forename: Jun-Shan
authors.initials: JS
authors.lastname: Ma
authors.affiliation: XinHua Hospital Affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, People's Republic of China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Shen
authors.affiliation: Lab of Big Data Analyses and Process, Information Science and Technology, Shanghai Sanda University, 2727 Jinhai Road, Pudong New District, Shanghai, 201209, People's Republic of China. tongyiichen@163.com.
authors.forename: Ying
authors.initials: Y
authors.lastname: Chen
mesh_terms: D002648:Child; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D020388:Muscular Dystrophy, Duchenne / Q000175:diagnosis*; D012372:ROC Curve; D060388:Support Vector Machine
keywords: Classifier; Duchenne muscular dystrophy; Performance; Support vector machines