Abstract

Info

Title:: Segmentation of skin lesion image based on multi-scale encoder-decoder network

Author(s):: YANG Guoliang; HONG Zhiyang; XU Nan; School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China

Keywords:: ; Keywords: skin lesion; multi-scale encoder-decoder network; SegNet; binary bilinear interpolation

Abstract:: Abstract: An image segmentation algorithm based on multi-scale encoder-decoder network is proposed for the segmentation of skin lesion image in medical diagnosis. The proposed algorithm inherits the characteristics of SegNet network structure, such as fast training speed and small training model storage. And the multi-scale input method enhances the ability of network to comprehensively learn the skin lesion image. In addition, the output of a layer of binary bilinear interpolated intermediate prediction features to the final layer of convolution blocks of the decoder layer in the pool2 layer of the encoder network is cascaded to increase the final segmentation accuracy. The experimental results show that using multi-scale encoder-decoder network can achieve an excellent segmentation of skin lesion image, and that the proposed network can also be widely used in other medical image segmentations.