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...

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Bibliographic Details
Published in:Journal of the American Academy of Dermatology 2015-11, Vol.73 (5), p.769-776
Main Authors: Ferris, Laura K., MD, PhD, Harkes, Jan A., MS, Gilbert, Benjamin, MS, Winger, Daniel G., MS, Golubets, Kseniya, MD, MHS, Akilov, Oleg, MD, PhD, Satyanarayanan, Mahadev, PhD
Format: Article
Language:English
Subjects:
basal cell carcinoma
computer-assisted diagnosis
Decision Trees
Dermatology
dermoscopy
Dermoscopy - methods
Female
Humans
Image Interpretation, Computer-Assisted - methods
information technology
machine learning
Male
melanoma
Melanoma - classification
Melanoma - pathology
skin cancer
Skin Neoplasms - classification
Skin Neoplasms - pathology
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Summary: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  
ISSN:0190-9622
1097-6787
DOI:10.1016/j.jaad.2015.07.028