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.
关键词
深度学习/皮肤镜图像分割/卷积神经网络/注意力机制
Key words
deep learning/dermatoscopic image segmentation/convolutional neural network/attention mechanism
维普
万方数据