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Incorporating clinical metadata with digital image features for automated identification of cutaneous melanoma
Author(s) -
Liu Z.,
Sun J.,
Smith M.,
Smith L.,
Warr R.
Publication year - 2013
Publication title -
british journal of dermatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.304
H-Index - 179
eISSN - 1365-2133
pISSN - 0007-0963
DOI - 10.1111/bjd.12550
Subject(s) - metadata , computer science , cad , identification (biology) , computer aided diagnosis , process (computing) , artificial intelligence , world wide web , engineering drawing , biology , botany , engineering , operating system
Summary Background 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. Objectives 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. Methods 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 L aplacian eigenmap, a dimensionality‐reduction method grouping lesions with similar digital features and metadata into the same classes. Results 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. Conclusions 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.