This chapter introduces a computer-aided method to detect skin lesion using
image features and shape features. Artificial neural networks (ANNs) trained with
image features (energy, contrast, homogeneity and correlation) and shape features
(asymmetry, border irregularity, color and diameter) in differentiating common nevus,
atypical nevus and melanoma using dermoscopy images were described. 120
dermoscopy skin lesion images were collected from online PH2 database. The model
was built on a single 3 layers, feed forward back propagation ANNs trained and tested
with round robin method. The ANN’s performance was evaluated with receiver
operating characteristic (ROC) analysis and chi-square test. The performance was
evaluated by comparing total dermoscopy score method with ANNs method. Our result
noted that the area under curve (Az) of ROC were 0.807 for differentiating atypical
nevus from common nevus, 0.998 for differentiating melanoma from common nevus
and 0.959 for i differentiating melanoma from atypical nevus, respectively. This
indicated that the ANNs method provided an accurate differential diagnosis in common
skin lesions for dermoscopy images.
