  This chapter presents a methodology for diagnosis of pigmented skin lesions
using convolutional neural networks. The architecture is based on
convolu-tional neural networks and it is evaluated using new CNN models as well
as re-trained modification of pre-existing CNN models were used. The
experi-mental results showed that CNN models pre-trained on big datasets for
gen-eral purpose image classification when re-trained in order to identify skin
le-sion types offer more accurate results when compared to convolutional neural
network models trained explicitly from the dermatoscopic images. The best
performance was achieved by re-training a modified version of ResNet-50
convolutional neural network with accuracy equal to 93.89%. Analysis on skin
lesion pathology type was also performed with classification accuracy for
melanoma and basal cell carcinoma being equal to 79.13% and 82.88%,
respectively.
