Analysis of clinical and dermoscopic features for basal cell carcinoma neural network classification

Authors


Address:

R. Joe Stanley

Department of Electrical and Computer Engineering

Missouri University of Science and Technology (S&T)

Rolla, MO 65409

USA

Tel: +1 573 341 6896

Fax: +1 573 341 4532

e-mail: stanleyj@mst.edu

Abstract

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.

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