title: Computer-aided pattern classification system for dermoscopy images.
abstract: BACKGROUND
  Computer-aided pattern classification of melanoma and other pigmented skin lesions is one of the most important tasks for clinical diagnosis. To differentiate between benign and malignant lesions, the extraction of color, architectural order, symmetry of pattern and homogeneity (CASH) is a challenging task.
  
  
  METHODS
  In this article, a novel pattern classification system (PCS) based on the clinical CASH rule is presented to classify among six classes of patterns. The PCS system consists of the following five steps: transformation to the CIE L*a*b* color space, pre-processing to enhance the tumor region and removal of hairs, tumor-area segmentation, color and texture feature extraction, and finally, classification based on a multiclass support vector machine.
  
  
  RESULTS
  The PCS system is tested on a total of 180 dermoscopic images. To test the performance of the PCS diagnostic classifier, the area under the receiver operating characteristics curve (AUC) is utilized. The proposed classifier achieved a sensitivity of 91.64%, specificity of 94.14%, and AUC of 0.948.
  
  
  CONCLUSIONS
  The experimental results demonstrate that the proposed pattern classifier is highly accurate and classify between benign and malignant lesions into some extend. The PCS method is fully automatic and can accurately detect different patterns from dermoscopy images using color and texture properties. Additional pattern features can be included to investigate the impact of pattern classification based on the CASH rule.
authors.affiliation: Department of Computer Science, National Textile University, Faisalaba-37610, Pakistan. drqaisar@ntu.edu.pk
authors.forename: Qaisar
authors.initials: Q
authors.lastname: Abbas
authors.forename: M Emre
authors.initials: ME
authors.lastname: Celebi
authors.forename: Irene
authors.initials: I
authors.lastname: Fondón
mesh_terms: D000328:Adult; D000369:Aged, 80 and over; D001185:Artificial Intelligence; D046169:Dermoscopy / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*