融合残差Inception与双向ConvGRU的皮肤病变智能分割

Residual Inception and Bidirectional ConvGRU Empowered Intelligent Segmentation for Skin Lesion

由于皮肤病病灶的形状、颜色以及纹理差异极大,且边界不明确,使得传统深度学习方法很难对其进行准确分割。因此本文提出了一种融合残差Inception与双向卷积门控循环单元(Convolutional gated recurrent unit,ConvGRU)的皮肤病变智能分割模型。首先设计了一种云边协同的皮肤病变智能分割服务网络模型,通过该网络模型,用户可以获得快速、准确的分割服务;其次,构建了一种新的皮肤病变智能分割模型,通过融合残差Inception与双向ConvGRU,该模型能融合不同尺度特征,提高模型特征提取能力,并能充分利用底层特征与语义特征之间的关系,捕获更丰富的全局上下文信息,取得更好的分割性能;最后,在ISIC 2018数据集上的实验结果表明,所提出的智能分割模型与近期提出的几种U-Net扩展模型相比,取得了更高的准确率与Jaccard系数。

The shape,color and texture of skin lesions are very different,and the boundaries are not clear,which makes it difficult for the traditional deep learning methods to segment them accurately.Based on the above challenge,this paper proposes a residual Inception and bidirectional convolutional gated recurrent unit(ConvGRU)empowered intelligent segmentation model for skin lesion.Specifically,a cloud-edge collaboration intelligent segmentation service network model for skin lesion is firstly designed.By this network model,users can obtain quick and accurate segmentation services.Furthermore,a novel intelligent segmentation model for skin lesion is developed.By integrating residual Inception and bidirectional ConvGRU,this model can fuse multi-scale features and make full use of the relationship between low-level features and semantic features.It improves the ability of the model to extract features and capture global context information,and leads to better segmentation performance.Finally,experimental results on ISIC 2018 dataset show that the proposed intelligent segmentation model achieves higher accuracy and Jaccard coefficient than several recently proposed U⁃Net extended models.