Computer-aided classification of melanocytic lesions using dermoscopic images

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. Limitations: 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.