摘要
针对皮肤病变图像边界分割不准确的问题,提出了一种改进的稠密卷积网络(DenseNet-BC)皮肤损伤分割算法。首先,改变传统算法层与层之间的连接方式,通过密集连接使得所有层都能直接访问从原始输入信号到损失函数的梯度,让图像特征信息得到最大化的流动。其次,为降低参数数量与网络的计算量,在瓶颈层和过渡层中采用小卷积核对输入特征图的通道数进行减半操作。将DenseNet-BC算法与VGG-16、Inception-v3以及ResNet-50等算法在ISIC 2018 Task 1皮肤病变分割数据集上进行性能比较。实验结果表明,DenseNet-BC算法的病变分割准确率为0.975,Threshold Jaccard为0.835,分割准确率较其他算法提升显著,是一种有效的皮损分割算法。
In order to solve the problem of inaccurate boundary segmentation of skin lesion images,an improved skin lesion segmentation algorithm based on dense lesion convolution network(DenseNet-BC)is proposed.Firstly,the connection way between the traditional algorithm layers is changed.Through dense connections,all layers can directly access the gradient from the original input signal to the loss function,so as to maximize the image feature information.Secondly,in order to reduce the number of parameters and the calculation amount of the network,the small convolution kernel is used in the bottleneck layer and the transition layer to halve the number of channels of the input feature map.The performance of this method is compared with the algorithms of VGG-16,Inception-v3 and ResNet-50 on the ISIC 2018 task 1 skin lesion segmentation data set.The experimental results show that the DenseNet-BC algorithm has the segmentation accuracy of 0.975 and the Threshold Jaccard of 0.835.The segmentation accuracy is significantly improved compared with other algorithms,and it is an effective method for skin lesion segmentation.
作者
齐永锋
侯璐璐
段友放
QI Yong-feng;HOU Lu-lu;DUAN You-fang(School of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070,China)
出处
《计算机工程与科学》
CSCD
北大核心
2020年第6期1060-1067,共8页
Computer Engineering & Science
基金
国家自然科学基金(61561044)。
关键词
皮肤镜图像
皮损分割
深度学习
稠密卷积网络
dermoscopic image
skin lesion segmentation
deep learning
dense convolutional network
