Research progress on the application of fused attention convolutional neural networks in dermatoscopic segmentation
10.16753/j.cnki.1008-2344.2024.05.014
- VernacularTitle:融合注意力卷积神经网络在皮肤镜分割中的应用研究进展
- Author:
Xiaonan SUN
1
;
Kui LU
;
Chen CHEN
;
Jiangshan SUN
;
Qiyue ZHU
Author Information
1. 安徽理工大学计算机科学与工程学院,安徽 淮南 232001
- Keywords:
deep learning;
dermatoscopic image segmentation;
convolutional neural network;
attention mechanism
- From:
Journal of Shenyang Medical College
2024;26(5):514-523
- CountryChina
- Language:Chinese
-
Abstract:
In automatic skin damage analysis,segmentation is a challenging and critical operation due to factors such as the shape and contrast of hair and skin lesions on the skin.Compared with traditional segmentation methods,deep learning seamlessly integrates feature extraction and task-specific decision-making,achieving segmentation tasks more accurately and efficiently,and effectively reducing the burden and cost of skin cancer screening.This article first introduces the background of dermoscopic segmentation and deep learning models,and introduces the application of deep learning in dermoscopic segmentation.Secondly,this article introduces the algorithm models of convolutional neural networks and attention mechanisms,reviews the application of fused attention convolutional neural networks in dermoscopic segmentation since Jan 2022,and summarizes the improvement strategies,the advantages and disadvantages of the model.The model is further analyzed based on commonly used datasets of dermoscopy and evaluation indicators of image segmentation.Finally,the application of fused attention convolutional neural network in dermoscopic segmentation is summarized and prospected.
