Funding sources This project was supported by the Technology Strategy Board under the grant number TP/6/ICT/6/S/K1524H.
Clinical and Laboratory Investigations
Incorporating clinical metadata with digital image features for automated identification of cutaneous melanoma
Article first published online: 31 OCT 2013
© 2013 British Association of Dermatologists
British Journal of Dermatology
Volume 169, Issue 5, pages 1034–1040, November 2013
How to Cite
Liu, Z., Sun, J., Smith, M., Smith, L. and Warr, R. (2013), Incorporating clinical metadata with digital image features for automated identification of cutaneous melanoma. British Journal of Dermatology, 169: 1034–1040. doi: 10.1111/bjd.12550
Conflicts of interest None declared.
- Issue published online: 31 OCT 2013
- Article first published online: 31 OCT 2013
- Accepted manuscript online: 31 JUL 2013 10:52PM EST
- Manuscript Accepted: 24 JUN 2013
- Technology Strategy Board. Grant Number: TP/6/ICT/6/S/K1524H
Computer-assisted diagnosis (CAD) of malignant melanoma (MM) has been advocated to help clinicians to achieve a more objective and reliable assessment. However, conventional CAD systems examine only the features extracted from digital photographs of lesions. Failure to incorporate patients' personal information constrains the applicability in clinical settings.
To develop a new CAD system to improve the performance of automatic diagnosis of melanoma, which, for the first time, incorporates digital features of lesions with important patient metadata into a learning process.
Thirty-two features were extracted from digital photographs to characterize skin lesions. Patients' personal information, such as age, gender and, lesion site, and their combinations, was quantified as metadata. The integration of digital features and metadata was realized through an extended Laplacian eigenmap, a dimensionality-reduction method grouping lesions with similar digital features and metadata into the same classes.
The diagnosis reached 82·1% sensitivity and 86·1% specificity when only multidimensional digital features were used, but improved to 95·2% sensitivity and 91·0% specificity after metadata were incorporated appropriately. The proposed system achieves a level of sensitivity comparable with experienced dermatologists aided by conventional dermoscopes. This demonstrates the potential of our method for assisting clinicians in diagnosing melanoma, and the benefit it could provide to patients and hospitals by greatly reducing unnecessary excisions of benign naevi.
This paper proposes an enhanced CAD system incorporating clinical metadata into the learning process for automatic classification of melanoma. Results demonstrate that the additional metadata and the mechanism to incorporate them are useful for improving CAD of melanoma.