Analysis of Clinical and Dermoscopic Features for Basal Cell Carcinoma Neural Network Classification


Background: Basal cell carcinoma (BCC) is the most commonly diagnosed cancer in the USA. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features. Methods: Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including evolving artificial neural networks (EANNs) and evolving artificial neural network ensembles. Results: Experiment results based on 10-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories. Conclusions: Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process.


Electrical and Computer Engineering

Second Department


Keywords and Phrases

Artificial neural network ensembles; Basal cell carcinoma; Benign lesion; Cross validation; Data sets; Dermoscopy; Diagnostic decisions; Diagnostic process; Image information; Information sources; Lesion discrimination; Lesion size; Network-based; Neural network classification; Personal profile; Receiver operating characteristic curves; Skin lesion; Training and testing; Artificial intelligence; Data fusion; Image processing; Neural networks; Diagnosis; artificial neural network; cancer classification; clinical feature; epiluminescence microscopy; human; mathematical computing; receiver operating characteristic; validation process; Adult; Aged; Algorithms; Carcinoma; Basal Cell; Color; Female; Humans; Male; Middle Aged; Neural Networks (Computer); ROC Curve; Skin; Skin Neoplasms; Skin Ulcer; Telangiectasis; Computational intelligence; Neural network

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Article - Journal

Document Version


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© 2013 John Wiley & Sons, All rights reserved.

Publication Date

01 Feb 2013