  Melanoma is clinically difficult to distinguish from common benign skin
lesions, particularly melanocytic naevus and seborrhoeic keratosis. The
dermoscopic appearance of these lesions has huge intra-class variations and
high inter-class visual similarities. Most current research is focusing on
single-class segmentation irrespective of classes of skin lesions. In this
work, we evaluate the performance of deep learning on multi-class segmentation
of ISIC-2017 challenge dataset, which consists of 2,750 dermoscopic images. We
propose an end-to-end solution using fully convolutional networks (FCNs) for
multi-class semantic segmentation to automatically segment the melanoma,
seborrhoeic keratosis and naevus. To improve the performance of FCNs, transfer
learning and a hybrid loss function are used. We evaluate the performance of
the deep learning segmentation methods for multi-class segmentation and lesion
diagnosis (with post-processing method) on the testing set of the ISIC-2017
challenge dataset. The results showed that the two-tier level transfer learning
FCN-8s achieved the overall best result with \textit{Dice} score of 78.5% in a
naevus category, 65.3% in melanoma, and 55.7% in seborrhoeic keratosis in
multi-class segmentation and Accuracy of 84.62% for recognition of melanoma in
lesion diagnosis.
