Application of a parallel branches network based on Transformer for skin melanoma segmentation.
10.7507/1001-5515.202110073
- Author:
Sanli YI
1
;
Gang ZHANG
1
;
Jianfeng HE
1
Author Information
1. School of Information Engineering and Automation, Kunming University of Scienceand Technology, Kunming 650500, P. R. China.
- Publication Type:Journal Article
- Keywords:
Computer vision;
Deep learning;
Skin segmentation;
Transformer
- MeSH:
Humans;
Dermoscopy/methods*;
Neural Networks, Computer;
Melanoma/pathology*;
Skin Neoplasms/pathology*;
Image Processing, Computer-Assisted/methods*
- From:
Journal of Biomedical Engineering
2022;39(5):937-944
- CountryChina
- Language:Chinese
-
Abstract:
Cutaneous malignant melanoma is a common malignant tumor. Accurate segmentation of the lesion area is extremely important for early diagnosis of the disease. In order to achieve more effective and accurate segmentation of skin lesions, a parallel network architecture based on Transformer is proposed in this paper. This network is composed of two parallel branches: the former is the newly constructed multiple residual frequency channel attention network (MFC), and the latter is the visual transformer network (ViT). First, in the MFC network branch, the multiple residual module and the frequency channel attention module (FCA) module are fused to improve the robustness of the network and enhance the capability of extracting image detailed features. Second, in the ViT network branch, multiple head self-attention (MSA) in Transformer is used to preserve the global features of the image. Finally, the feature information extracted from the two branches are combined in parallel to realize image segmentation more effectively. To verify the proposed algorithm, we conducted experiments on the dermoscopy image dataset published by the International Skin Imaging Collaboration (ISIC) in 2018. The results show that the intersection-over-union (IoU) and Dice coefficients of the proposed algorithm achieve 90.15% and 94.82%, respectively, which are better than the latest skin melanoma segmentation networks. Therefore, the proposed network can better segment the lesion area and provide dermatologists with more accurate lesion data.
