  Automatic segmentation of skin lesion is considered a crucial step in
Computer Aided Diagnosis (CAD) for melanoma diagnosis. Despite its
significance, skin lesion segmentation remains a challenging task due to their
diverse color, texture, and indistinguishable boundaries and forms an open
problem. Through this study, we present a new and automatic semantic
segmentation network for robust skin lesion segmentation named Dermoscopic Skin
Network (DSNet). In order to reduce the number of parameters to make the
network lightweight, we used depth-wise separable convolution in lieu of
standard convolution to project the learned discriminating features onto the
pixel space at different stages of the encoder. Additionally, we implemented
U-Net and Fully Convolutional Network (FCN8s) to compare against the proposed
DSNet. We evaluate our proposed model on two publicly available datasets,
namely ISIC-2017 and PH2. The obtained mean Intersection over Union (mIoU) is
77.5 % and 87.0 % respectively for ISIC-2017 and PH2 datasets which
outperformed the ISIC-2017 challenge winner by 1.0 % with respect to mIoU. Our
proposed network also outperformed U-Net and FCN8s respectively by 3.6 % and
6.8 % with respect to mIoU on the ISIC-2017 dataset. Our network for skin
lesion segmentation outperforms other methods and can provide better segmented
masks on two different test datasets which can lead to better performance in
melanoma detection. Our trained model along with the source code and predicted
masks are made publicly available.

Image and Video Processing;Computer Vision and Pattern Recognition