  There has been a steady increase in the incidence of skin cancer worldwide,
with a high rate of mortality. Early detection and segmentation of skin lesions
are crucial for timely diagnosis and treatment, necessary to improve the
survival rate of patients. However, skin lesion segmentation is a challenging
task due to the low contrast of lesions and their high similarity in terms of
appearance, to healthy tissue. This underlines the need for an accurate and
automatic approach for skin lesion segmentation. To tackle this issue, we
propose a convolutional neural network (CNN) called SkinNet. The proposed CNN
is a modified version of U-Net. We compared the performance of our approach
with other state-of-the-art techniques, using the ISBI 2017 challenge dataset.
Our approach outperformed the others in terms of the Dice coefficient, Jaccard
index and sensitivity, evaluated on the held-out challenge test data set,
across 5-fold cross validation experiments. SkinNet achieved an average value
of 85.10, 76.67 and 93.0%, for the DC, JI, and SE, respectively.
