  Automatic lesion analysis is critical in skin cancer diagnosis and ensures
effective treatment. The computer aided diagnosis of such skin cancer in
dermoscopic images can significantly reduce the clinicians workload and help
improve diagnostic accuracy. Although researchers are working extensively to
address this problem, early detection and accurate identification of skin
lesions remain challenging. This research focuses on a two step framework for
skin lesion segmentation followed by classification for lesion analysis. We
explored the effectiveness of deep convolutional neural network based
architectures by designing an encoder-decoder architecture for skin lesion
segmentation and CNN based classification network. The proposed approaches are
evaluated quantitatively in terms of the Accuracy, mean Intersection over Union
and Dice Similarity Coefficient. Our cascaded end to end deep learning based
approach is the first of its kind, where the classification accuracy of the
lesion is significantly improved because of prior segmentation.
