title: Computer-aided classification of melanocytic lesions using dermoscopic images.
abstract: BACKGROUND
  Computer-assisted diagnosis of dermoscopic images of skin lesions has the potential to improve melanoma early detection.
  
  
  OBJECTIVE
  We sought to evaluate the performance of a novel classifier that uses decision forest classification of dermoscopic images to generate a lesion severity score.
  
  
  METHODS
  Severity scores were calculated for 173 dermoscopic images of skin lesions with known histologic diagnosis (39 melanomas, 14 nonmelanoma skin cancers, and 120 benign lesions). A threshold score was used to measure classifier sensitivity and specificity. A reader study was conducted to compare the sensitivity and specificity of the classifier with those of 30 dermatology clinicians.
  
  
  RESULTS
  The classifier sensitivity for melanoma was 97.4%; specificity was 44.2% in a test set of images. In the reader study, the classifier's sensitivity to melanoma was higher (P < .001) and specificity was lower (P < .001) than that of clinicians.
  
  
  CONCLUSIONS
  This is a retrospective study using existing images primarily chosen for biopsy by a dermatologist. The size of the test set is small.
  
  
  CONCLUSIONS
  Our classifier may aid clinicians in deciding if a skin lesion should be biopsied and can easily be incorporated into a portable tool (that uses no proprietary equipment) that could aid clinicians in noninvasively evaluating cutaneous lesions.
authors.affiliation: Department of Dermatology, University of Pittsburgh, Pittsburgh, Pennsylvania. Electronic address: ferrislk@upmc.edu.
authors.forename: Laura K
authors.initials: LK
authors.lastname: Ferris
authors.affiliation: Carnegie Mellon University, Computer Science Department, Pittsburgh, Pennsylvania.
authors.forename: Jan A
authors.initials: JA
authors.lastname: Harkes
authors.affiliation: Carnegie Mellon University, Computer Science Department, Pittsburgh, Pennsylvania.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Gilbert
authors.affiliation: Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: Daniel G
authors.initials: DG
authors.lastname: Winger
authors.affiliation: Department of Dermatology, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: Kseniya
authors.initials: K
authors.lastname: Golubets
authors.affiliation: Department of Dermatology, University of Pittsburgh, Pittsburgh, Pennsylvania.
authors.forename: Oleg
authors.initials: O
authors.lastname: Akilov
authors.affiliation: Carnegie Mellon University, Computer Science Department, Pittsburgh, Pennsylvania.
authors.forename: Mahadev
authors.initials: M
authors.lastname: Satyanarayanan
mesh_terms: D003663:Decision Trees; D046169:Dermoscopy / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008545:Melanoma / Q000145:classification* / Q000473:pathology; D012878:Skin Neoplasms / Q000145:classification* / Q000473:pathology
keywords: basal cell carcinoma; computer-assisted diagnosis; dermoscopy; information technology; machine learning; melanoma; skin cancer