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基于双重注意力机制的皮肤病变图像分割算法

IMAGE SEGMENTATION ALGORITHM FOR SKIN LESIONS BASED ON DUAL ATTENTION MECHANISM
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摘要 针对黑素瘤存在难以分割,毛发遮挡时分割效果不佳,提出一种基于双重注意力机制的皮肤病变图像分割神经网络。模型共有两个解码路径与一个编码路径。首先将图像经过预处理与数据增强后采用ResNet50主干提取网络获得不同分辨率大小的特征层,再通过首个编码路径,对提取的最后一层特征层进行上采样后与之前提取的特征层进行特征融合,随后进入接下来的编码解码路径,最后通过RAB空间与通道注意力模块得到最终输出。在ISBI2016皮肤病变图像数据集上进行多次对比与消融实验,实验结果表明对于被毛发或其他物体遮挡的图像有着优秀分割结果。实验各项指标分别为准确率96.19%、敏感度93.32%、特异性97.32%、Dice系数93.26%和Jaccard系数87.36%,均优于现有算法。 In view of the difficulty of melanoma segmentation and the poor segmentation effect in the presence of hair covering,a neural network for skin lesion image segmentation based on dual attention mechanism is proposed,which has two decoding paths and one encoding path.The image was preprocessed and data-enhanced,and the Resnet50 backbone extraction network was used to obtain the feature layer of different resolution sizes.The last feature layer extracted was sampled and fused with the previously extracted feature layer through the first coding path,and the next coding and decoding path was entered.The final output was obtained through RAB space and channel attention module.Comparison and ablation experiments were performed on ISBI2016 skin lesion image dataset for several times.According to the experimental results,excellent segmentation results were obtained for images blocked by hair or other objects.The indexes of the experiment are as follows:accuracy 96.19%,sensitivity 93.32%,specificity 97.32%,Dice coefficient 93.26%and Jaccard coefficient 87.36%,which are all superior to the existing algorithms.
作者 邝先验 陈奕希 刘平 张建华 Kuang Xianyan;Chen Yixi;Liu Ping;Zhang Jianhua(School of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou 341000,Jiangxi,China)
出处 《计算机应用与软件》 北大核心 2024年第11期261-267,共7页 Computer Applications and Software
关键词 黑素瘤 双重注意力机制 卷积神经网络 图像分割 Melanoma Dual attention mechanism Convolutional neural network Image segmentation
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