  Early detection of skin cancer, particularly melanoma, is crucial to enable
advanced treatment. Due to the rapid growth in the numbers of skin cancers,
there is a growing need of computerized analysis for skin lesions. The
state-of-the-art public available datasets for skin lesions are often
accompanied with very limited amount of segmentation ground truth labeling as
it is laborious and expensive. The lesion boundary segmentation is vital to
locate the lesion accurately in dermoscopic images and lesion diagnosis of
different skin lesion types. In this work, we propose the use of fully
automated deep learning ensemble methods for accurate lesion boundary
segmentation in dermoscopic images. We trained the Mask-RCNN and DeepLabv3+
methods on ISIC-2017 segmentation training set and evaluate the performance of
the ensemble networks on ISIC-2017 testing set. Our results showed that the
best proposed ensemble method segmented the skin lesions with Jaccard index of
79.58% for the ISIC-2017 testing set. The proposed ensemble method outperformed
FrCN, FCN, U-Net, and SegNet in Jaccard Index by 2.48%, 7.42%, 17.95%, and
9.96% respectively. Furthermore, the proposed ensemble method achieved an
accuracy of 95.6% for some representative clinically benign cases, 90.78% for
the melanoma cases, and 91.29% for the seborrheic keratosis cases on ISIC-2017
testing set, exhibiting better performance than FrCN, FCN, U-Net, and SegNet.
