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J Am Acad Dermatol. 2015 Nov;73(5):769-76. doi: 10.1016/j.jaad.2015.07.028. Epub 2015 Sep 19.

Computer-aided classification of melanocytic lesions using dermoscopic images.

Author information

1
Department of Dermatology, University of Pittsburgh, Pittsburgh, Pennsylvania. Electronic address: ferrislk@upmc.edu.
2
Carnegie Mellon University, Computer Science Department, Pittsburgh, Pennsylvania.
3
Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
4
Department of Dermatology, University of Pittsburgh, Pittsburgh, Pennsylvania.

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.

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.

KEYWORDS:

basal cell carcinoma; computer-assisted diagnosis; dermoscopy; information technology; machine learning; melanoma; skin cancer

PMID:
26386631
DOI:
10.1016/j.jaad.2015.07.028
[Indexed for MEDLINE]

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