  Skin cancer is a frequently occurring cancer in the human population, and it
is very important to be able to diagnose malignant tumors in the body early.
Lesion segmentation is crucial for monitoring the morphological changes of skin
lesions, extracting features to localize and identify diseases to assist
doctors in early diagnosis. Manual de-segmentation of dermoscopic images is
error-prone and time-consuming, thus there is a pressing demand for precise and
automated segmentation algorithms. Inspired by advanced mechanisms such as
U-Net, DenseNet, Separable Convolution, Channel Attention, and Atrous Spatial
Pyramid Pooling (ASPP), we propose a novel network called Channel Attention
Separable Convolution Network (CASCN) for skin lesions segmentation. The
proposed CASCN is evaluated on the PH2 dataset with limited images. Without
excessive pre-/post-processing of images, CASCN achieves state-of-the-art
performance on the PH2 dataset with Dice similarity coefficient of 0.9461 and
accuracy of 0.9645.
