Attention-based dual-path feature fusion network for automatic skin lesion segmentation

BioData Mining

Research

Nianzu Lv¹  Xiaoxia Li¹  Yuling Chen¹  Zhenxiang He²  Yong Cai³ 

[1] School of Information Engineering, Southwest University of Science and Technology, Mianyang, China;Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang, China;School of Information Engineering, Southwest University of Science and Technology, Mianyang, China;Tianfu College of Southwest University of Finance and Economics, Mianyang, China;School of manufacturing science and Engineering, Southwest University of Science and Technology, Mianyang, China;

关键词: Skin lesion segmentation;    Convolution neural network;    Boundary refinement;   

DOI  :  10.1186/s13040-023-00345-x

 received in 2023-04-10, accepted in 2023-09-27,  发布年份 2023

来源: Springer

【 摘 要 】

Automatic segmentation of skin lesions is a critical step in Computer Aided Diagnosis (CAD) of melanoma. However, due to the blurring of the lesion boundary, uneven color distribution, and low image contrast, resulting in poor segmentation result. Aiming at the problem of difficult segmentation of skin lesions, this paper proposes an Attention-based Dual-path Feature Fusion Network (ADFFNet) for automatic skin lesion segmentation. Firstly, in the spatial path, a Boundary Refinement (BR) module is designed for the output of low-level features to filter out irrelevant background information and retain more boundary details of the lesion area. Secondly, in the context path, a Multi-scale Feature Selection (MFS) module is constructed for high-level feature output to capture multi-scale context information and use the attention mechanism to filter out redundant semantic information. Finally, we design a Dual-path Feature Fusion (DFF) module, which uses high-level global attention information to guide the step-by-step fusion of high-level semantic features and low-level detail features, which is beneficial to restore image detail information and further improve the pixel-level segmentation accuracy of skin lesion. In the experiment, the ISIC 2018 and PH2 datasets are employed to evaluate the effectiveness of the proposed method. It achieves a performance of 0.890/ 0.925 and 0.933 /0.954 on the F1-score and SE index, respectively. Comparative analysis with state-of-the-art segmentation methods reveals that the ADFFNet algorithm exhibits superior segmentation performance.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

12936_2023_4742_Article_IEq20.gif

Fig. 1

12936_2023_4742_Article_IEq18.gif

Fig. 2

Fig. 3

Fig. 4

Fig. 6

Fig. 3

Fig. 3

Fig. 7

Fig. 1

Fig. 1

12936_2023_4742_Article_IEq9.gif

Fig. 6

Fig. 2

Fig. 4

Fig. 8

Fig. 2

Fig. 5

Fig. 4

12936_2016_1411_Article_IEq110.gif

Fig. 3

Fig. 1

Fig. 2

Fig. 4

Fig. 2

Fig. 1

12888_2023_5283_Article_IEq2.gif

12888_2023_5283_Article_IEq1.gif

Fig. 1

12936_2017_1963_Article_IEq54.gif

Fig. 3

Fig. 1

12936_2015_1050_Article_IEq030.gif

Fig. 2

12936_2017_2118_Article_IEq1.gif

12951_2017_255_Article_IEq56.gif

Fig. 2

Fig. 3

Fig. 1

Fig. 5

Fig. 14

Fig. 11

Fig. 7

Fig. 3

12951_2017_283_Article_IEq3.gif

Fig. 13

Fig. 4

Fig. 3

Fig. 2

12951_2017_255_Article_IEq54.gif

12951_2017_255_Article_IEq53.gif

Fig. 2

12888_2023_5292_Article_IEq1.gif

Fig. 1

Fig. 6

Fig. 1

Fig. 1

12951_2017_255_Article_IEq48.gif

Fig. 3

Fig. 5

Fig. 1

Fig. 2

Fig. 10

Fig. 1

Fig. 4

Fig. 2

Fig. 8

12951_2016_246_Article_IEq7.gif

Fig. 1

12951_2016_246_Article_IEq6.gif

Fig. 8

Fig. 4

Fig. 1: The conceptual framework for adherence to treatment guidelines in private drug outlets in Kisumu, Kenya

Fig. 6

12951_2016_246_Article_IEq3.gif

12951_2016_246_Article_IEq1.gif

Fig. 1

Fig. 5