Efficient skin lesion segmentation using separable-Unet with stochastic weight averaging

•The deep learning technology approaches have been applied in the skin lesion segmentation.•A novel image-based separable-Unet network with stochastic weights averaging was proposed.•The proposed system in this paper exhibits stronger segmentation performance compared to the state-of-the-art with a lower computation cost.•To the best of our knowledge, this report for the first time describes the over-fitted problem of easy examples dominant as a problem of falling bad local optima, and introduce the stochastic weights averaging method to solve the problem, which can further boost our model to obtain SOTA performance.•Compared with existing methods, the proposed method can achieve both excellent performance in accuracy and real-time. Efficient segmentation of skin lesion in dermoscopy images can improve the classification accuracy of skin diseases, which provides a powerful approach for the dermatologists in examining pigmented skin lesions. However, the segmentation is challenging due to the low contrast of skin lesions from a captured image, fuzzy and indistinct lesion boundaries, huge variety of interclass variation of melanomas, the existence of artifacts, etc. In this work, an efficient and accurate melanoma region segmentation method is proposed for computer-aided diagnostic systems. A skin lesion segmentation (SLS) method based on the separable-Unet with stochastic weight averaging is proposed in this work. Specifically, the proposed Separable-Unet framework takes advantage of the separable convolutional block and U-Net architectures, which can extremely capture the context feature channel correlation and higher semantic feature information to enhance the pixel-level discriminative representation capability of fully convolutional networks (FCN). Further, considering that the over-fitting is a local optimum (or sub-optimum) problem, a scheme based on stochastic weight averaging is introduced, which can obtain much broader optimum and better generalization. The proposed method is evaluated in three publicly available datasets. The experimental results showed that the proposed approach segmented the skin lesions with an average Dice coefficient of 93.03% and Jaccard index of 89.25% for the International Skin Imaging Collaboration (ISIC) 2016 Skin Lesion Challenge (SLC) dataset, 86.93% and 79.26% for the ISIC 2017 SLC, and 94.13% and 89.40% for the PH2 dataset, respectively. The proposed approach is compared with other state-of-the-art methods, and the r