EA-Net: Research on skin lesion segmentation method based on U-Net

Dapeng Cheng; Jiale Gai; Yanyan Mao; Xiaolian Gao; Baosheng Zhang; Wanting Jing; Jia Deng; Feng Zhao; Ning Mao

doi:10.1016/j.heliyon.2023.e22663

EA-Net: Research on skin lesion segmentation method based on U-Net

Heliyon. 2023 Nov 22;9(12):e22663. doi: 10.1016/j.heliyon.2023.e22663. eCollection 2023 Dec.

Authors

Dapeng Cheng^{1

2}, Jiale Gai¹, Yanyan Mao¹, Xiaolian Gao¹, Baosheng Zhang¹, Wanting Jing³, Jia Deng¹, Feng Zhao^{1

2}, Ning Mao⁴

Affiliations

¹ School of Computer Science and Technology, Shandong Technology and Business University, Yantai, 264005, Shandong, China.
² Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, 264005, Shandong, China.
³ School of Information and Electronic Engineering, Shandong Business and Technology University, Yantai, 264005, Shandong, China.
⁴ Department of Radiology, Yantai Yuhuangding Hospital, Yantai, 264000, Shandong, China.

Abstract

Accurate segmentation of skin lesions is a challenging task because the task is highly influenced by factors such as location, shape and scale. In recent years, Convolutional Neural Networks (CNNs) have achieved advanced performance in automated medical image segmentation. However, existing CNNs have problems such as inability to highlight relevant features and preserve local features, which limit their application in clinical decision-making. This paper proposes a CNN with an added attention mechanism (EA-Net) for more accurate medical image segmentation.EA-Net is based on the U-Net network model framework. Specifically, we added a pixel-level attention module (PA) to the encoder section to preserve the local features of the image during downsampling, making the feature maps input to the decoder more relevant to the ground-truth. At the same time, we added a spatial multi-scale attention module (SA) after the decoding process to increase the spatial weight of the feature maps that are more relevant to the ground-truth, thereby reducing the gap between the output results and the ground-truth. We conducted extensive segmentation experiments on skin lesion images from the ISIC 2017 and ISIC 2018 datasets. The results demonstrate that, when compared to U-Net, our proposed EA-Net achieves an average Dice score improvement of 1.94% and 5.38% for skin lesion tissue segmentation on the ISIC 2017 and ISIC 2018 datasets, respectively. The IoU also increases by 2.69% and 8.31%, and the ASSD decreases by 0.3783 pix and 0.5432 pix, indicating superior segmentation performance. EA-Net can achieve better segmentation results when the original image of skin lesions has an obscure boundary and the segmentation area contains interference factors, which proves that the addition of attention mechanism in the encoder and the application of comprehensive attention mechanism can improve the performance of neural network in the field of skin lesions image segmentation.

Keywords: Attention mechanism; Convolutional neural network; Feature extraction; Image segmentation; Skin lesions.