  Skin lesions segmentation is an important step in the process of automated
diagnosis of the skin melanoma. However, the accuracy of segmenting melanomas
skin lesions is quite a challenging task due to less data for training,
irregular shapes, unclear boundaries, and different skin colors. Our proposed
approach helps in improving the accuracy of skin lesion segmentation. Firstly,
we have introduced the coordinate convolutional layer before passing the input
image into the encoder. This layer helps the network to decide on the features
related to translation invariance which further improves the generalization
capacity of the model. Secondly, we have leveraged the properties of deep
residual units along with the convolutional layers. At last, instead of using
only cross-entropy or Dice-loss, we have combined the two-loss functions to
optimize the training metrics which helps in converging the loss more quickly
and smoothly. After training and validating the proposed model on ISIC 2018
(60% as train set + 20% as validation set), we tested the robustness of our
trained model on various other datasets like ISIC 2018 (20% as test-set) ISIC
2017, 2016 and PH2 dataset. The results show that the proposed model either
outperform or at par with the existing skin lesion segmentation methods.
