Abstract

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Author(s):: WEI Kun1; SHEN Jiquan1; ZHAO Yanmei2; 1. College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 450000, China 2. Intensive Care Unit, Henan Childrens Hospital, Zhengzhou 460000, China

Keywords:: Keywords: medical image segmentation attention mechanism recognition of skin disease lightweight UNet

Abstract:: Abstract: The current depth learning segmentation algorithm has the problems of numerous parameters and high computational complexity. Therefore, a lightweight algorithm (MAUNet) which combines UNet and multiple attention mechanisms is proposed for skin lesion segmentation. The model integrates depth-wise separable convolution and gated attention mechanism modules on the basis of UNet to extract global and local feature information, adopts the external attention mechanism module to enhance the connection between samples, and uses the spatial and channel attention mechanism modules to extract channel and spatial features. The MAUNet model realizes feature extraction and classification on ISIC2017 skin disease public data set. Compared with the baseline model (UNet), the proposed model increases mIoU and DSC by 2.18% and 1.28% respectively, while reducing the number of parameters and computational complexity which were only 2.1% and 0.58% of the baseline model. The experimental results show that the model can balance the number of parameters, lower the computational complexity and perform well in segmentation and detection.