The growing incidence of skin cancer makes computer-aided diagnosis tools for this
group of diseases increasingly important. The use of ultrasound has the potential
to complement information from optical dermoscopy. The current work presents a fully
automatic classification framework utilizing fully-automated (FA) segmentation and
compares it with classification using two semi-automated (SA) segmentation methods.
Ultrasound recordings were taken from a total of 310 lesions (70 melanoma, 130 basal
cell carcinoma and 110 benign nevi). A support vector machine (SVM) model was trained
on 62 features, with ten-fold cross-validation. Six classification tasks were considered,
namely all the possible permutations of one class versus one or two remaining classes.
The receiver operating characteristic (ROC) area under the curve (AUC) as well as
the accuracy (ACC) were measured. The best classification was obtained for the classification
of nevi from cancerous lesions (melanoma, basal cell carcinoma), with AUCs of over
90% and ACCs of over 85% obtained with all segmentation methods. Previous works have
either not implemented FA ultrasound-based skin cancer classification (making diagnosis
more lengthy and operator-dependent), or are unclear in their classification results.
Furthermore, the current work is the first to assess the effect of implementing FA
instead of SA classification, with FA classification never degrading performance (in
terms of AUC or ACC) by more than 5%.
