[1]SHEN Xin,WEI Lisheng.Dermoscope image segmentation method based on ARB-UNet[J].CAAI Transactions on Intelligent Systems,2023,18(4):699-707.[doi:10.11992/tis.202201030]
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Dermoscope image segmentation method based on ARB-UNet

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 4 Page number: 699-707 Column: 学术论文—机器学习 Public date: 2023-07-15
Title:
Dermoscope image segmentation method based on ARB-UNet
Author(s):
SHEN Xin1;  WEI Lisheng2
1. School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China;
2. Anhui Key Laboratory of Electric Drive and Control, Anhui Polytechnic University, Wuhu 241000, China
Keywords:
image segmentation; dermoscopic; convolutional neural network; attention residual block-UNet(ARB-UNet); attention mechanism; convolutional block attention module(CBAM); deep learning; residual network
CLC:
TP391
DOI:
10.11992/tis.202201030
Abstract:
Aiming at the problems of intra-class difference, inter-class similarity, and dataset imbalance in dermoscopic images, a dermoscopic image segmentation method based on attention residual block-UNet (ARB-UNet) is proposed. Firstly, the convolutional block attention module (CBAM) is introduced into the “skip connection” of U-Net model; at the same time, the CBAM module is integrated into the residual module DRB (dilated residual networks) to obtain the attention residual block(ARB); Focal Tversky loss is selected as the loss function of the model; Finally, the proposed ARB-UNet model is trained and tested on ISIC2016 data set, and compared with traditional methods and classical methods such as U-Net. The experimental results show that the sensitivity (SE), specificity (SP), and dice similarity index (DSC) have reached 92.9%, 94.1%, and 92.1%, respectively, which are all better than other comparative methods in overall. Thus, the feasibility and effectiveness of the method in this paper are verified
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