Classification of pressure ulcer tissues with 3D convolutional neural network
Classification of pressure ulcer tissues with 3D convolutional neural network
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DOI：
10.1007/s11517-018-1835-y
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发表时间：
2018-12-01
影响因子：
3.2
通讯作者：
Elmaghraby, Adel S.
中科院分区：
文献类型：
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作者：
Garcia-Zapirain, Begona;Elmogy, Mohammed;Elmaghraby, Adel S.
为深度学习架构的3D卷积神经网络（CNN）提供基本的视觉特征，以准确分类和分割压疮彩色图像中的肉芽、坏死焦痂和斯劳组织。在找到感兴趣的区域（ROI）后，从原始图像中提取特征，并与预先选择的高斯核3D HSI图像卷积，结合当前和先前视觉外观的一阶模型。该模型用离散高斯线性组合（LCDG）逼近逐体素信号的经验边缘概率分布。该框架在193张彩色压疮图像上进行了训练和测试。使用Dice相似系数（DSC）、百分比面积距离（PAD）和ROC曲线下面积（AUC）评估分类准确性和稳健性。所获得的92%的初步DSC、13%的PAD和95%的AUC是有希望的。基于三维卷积神经网络的压疮组织分类。
A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from both the original and convolved with a pre-selected Gaussian kernel 3D HSI images, combined with first-order models of current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The framework was trained and tested on 193 color pressure ulcer images. The classification accuracy and robustness were evaluated using the Dice similarity coefficient (DSC), the percentage area distance (PAD), and the area under the ROC curve (AUC). The obtained preliminary DSC of 92%, PAD of 13%, and AUC of 95% are promising. The Classification of Pressure Ulcer Tissues Based on 3D Convolutional Neural Network.