Skin Lesion Segmentation Based on Modification of SegNet Neural Networks

Skin lesion segmentation plays an important role in automatic skin cancer diagnosis. One of the most dangerous types of this cancer is melanoma which requires an early and accurate detection. However, automatic melanoma segmentation on dermoscopic images is a challenging task since images are corrupted by noise like hairs, air bubbles, blood vessel… and with fuzzy boundaries. This paper presents a framework based on deep fully convolutional neural networks to automatically segment skin lesions in dermoscopic images. Particularly, the paper proposes a fully convolutional network (FCN) framework that is based on modification of the SegNet architecture. In particular, we propose to reduce the downsampling and upsampling layers in the original SegNet model, that reduces total learned parameters compared to the original SegNet model. The proposed approach is applied to segment images from ISIC 2017 dataset. Experimental results show the desired performances of the proposed approach in terms of metrics of Dice coefficient and Jaccard indexes.