MDSC-Net: A multi-scale depthwise separable convolutional neural network for skin lesion segmentation

Accurate segmentation of the skin lesion region is crucial for diagnosing and screening skin diseases. However, skin lesion segmentation is challenging due to the indistinguishable boundaries of the lesion region, irregular shapes and hair interference. To settle the above issues, we propose a Multi-scale Depthwise Separable Convolutional Neural Network for skin lesion segmentation named MDSC-Net. Specifically, a novel Multi-scale Depthwise Separable Residual Convolution Module is employed in skip connection, conveying more detailed features to the decoder. To compensate for the loss of spatial location information in down-sampling, we propose a novel Spatial Adaption Module. Furthermore, we propose a Multi-scale Decoding Fusion Module in the decoder to capture contextual information. We have performed extensive experiments to verify the effectiveness and robustness of the proposed network on three public benchmark skin lesion segmentation datasets and one public benchmark polyp segmentation dataset, including ISIC-2017, ISIC-2018, PH2, and Kvasir-SEG datasets. Experimental results consistently demonstrate the proposed MDSC-Net achieves superior segmentation across five popularly used evaluation criteria. The proposed network reaches high-performance skin lesion segmentation, and can provide important clues to help doctors diagnose and treat skin cancer early.