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.
Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA.
Beibei
B
Cheng
R
R
Joe Stanley
William V
WV
Stoecker
Sherea M
SM
Stricklin
Kristen A
KA
Hinton
Thanh K
TK
Nguyen
Ryan K
RK
Rader
Harold S
HS
Rabinovitz
Margaret
M
Oliviero
Randy H
RH
Moss
D000328:Adult; D000368:Aged; D000465:Algorithms; D002280:Basal Cell Carcinoma / Q000145:classification / Q000473:pathology*; D003116:Color; D046169:Dermoscopy / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D012867:Skin / Q000098:blood supply / Q000473:pathology; D012878:Skin Neoplasms / Q000145:classification / Q000473:pathology*; D012883:Skin Ulcer / Q000473:pathology; D013684:Telangiectasis / Q000473:pathology