PDF(Pubmed)
Abstract：
UNASSIGNED: To develop an advanced determination technology for detecting COVID-19 patterns from chest X-ray and CT-scan films with distinct applications of deep learning and machine learning methods.
UNASSIGNED: The newly enhanced proposed hybrid classification network (SVM-RLF-DNN) comprises of three phases: feature extraction, selection and classification. The in-depth features are extracted from a series of 3×3 convolution, 2×2 max polling operations followed by a flattened and fully connected layer of the deep neural network (DNN). ReLU activation function and Adam optimizer are used in the model. The ReliefF is an improved feature selection algorithm of Relief that uses Manhattan distance instead of Euclidean distance. Based on the significance of the feature, the ReliefF assigns weight to each extracted feature received from a fully connected layer. The weight to each feature is the average of k closest hits and misses in each class for a neighbouring instance pair in multiclass problems. The ReliefF eliminates lower-weight features by setting the node value to zero. The higher weights of the features are kept to obtain the feature selection. At the last layer of the neural network, the multiclass Support Vector Machine (SVM) is used to classify the patterns of COVID-19, viral pneumonia and healthy cases. The three classes with three binary SVM classifiers use linear kernel function for each binary SVM following a one-versus-all approach. The hinge loss function and L2-norm regularization are selected for more stable results. The proposed method is assessed on publicly available chest X-ray and CT-scan image databases from Kaggle and GitHub. The performance of the proposed classification model has comparable training, validation, and test accuracy, as well as sensitivity, specificity, and confusion matrix for quantitative evaluation on five-fold cross-validation.
UNASSIGNED: Our proposed network has achieved test accuracy of 98.48% and 95.34% on 2-class X-rays and CT. More importantly, the proposed model\'s test accuracy, sensitivity, and specificity are 87.9%, 86.32%, and 90.25% for 3-class classification (COVID-19, Pneumonia, Normal) on chest X-rays. The proposed model provides the test accuracy, sensitivity, and specificity of 95.34%, 94.12%, and 96.15% for 2-class classification (COVID-19, Non-COVID) on chest CT.
UNASSIGNED: Our proposed classification network experimental results indicate competitiveness with existing neural networks. The proposed neural network assists clinicians in determining and surveilling the disease.
UNASSIGNED: The newly enhanced proposed hybrid classification network (SVM-RLF-DNN) comprises of three phases: feature extraction, selection and classification. The in-depth features are extracted from a series of 3×3 convolution, 2×2 max polling operations followed by a flattened and fully connected layer of the deep neural network (DNN). ReLU activation function and Adam optimizer are used in the model. The ReliefF is an improved feature selection algorithm of Relief that uses Manhattan distance instead of Euclidean distance. Based on the significance of the feature, the ReliefF assigns weight to each extracted feature received from a fully connected layer. The weight to each feature is the average of k closest hits and misses in each class for a neighbouring instance pair in multiclass problems. The ReliefF eliminates lower-weight features by setting the node value to zero. The higher weights of the features are kept to obtain the feature selection. At the last layer of the neural network, the multiclass Support Vector Machine (SVM) is used to classify the patterns of COVID-19, viral pneumonia and healthy cases. The three classes with three binary SVM classifiers use linear kernel function for each binary SVM following a one-versus-all approach. The hinge loss function and L2-norm regularization are selected for more stable results. The proposed method is assessed on publicly available chest X-ray and CT-scan image databases from Kaggle and GitHub. The performance of the proposed classification model has comparable training, validation, and test accuracy, as well as sensitivity, specificity, and confusion matrix for quantitative evaluation on five-fold cross-validation.
UNASSIGNED: Our proposed network has achieved test accuracy of 98.48% and 95.34% on 2-class X-rays and CT. More importantly, the proposed model\'s test accuracy, sensitivity, and specificity are 87.9%, 86.32%, and 90.25% for 3-class classification (COVID-19, Pneumonia, Normal) on chest X-rays. The proposed model provides the test accuracy, sensitivity, and specificity of 95.34%, 94.12%, and 96.15% for 2-class classification (COVID-19, Non-COVID) on chest CT.
UNASSIGNED: Our proposed classification network experimental results indicate competitiveness with existing neural networks. The proposed neural network assists clinicians in determining and surveilling the disease.
摘要：
■开发一种先进的确定技术，通过深度学习和机器学习方法的不同应用，从胸部X射线和CT扫描胶片中检测COVID-19模式。
■新增强的混合分类网络（SVM-RLF-DNN）包括三个阶段：特征提取，选择和分类。从一系列3×3卷积中提取深度特征，2×2最大轮询操作，然后是深度神经网络(DNN)的扁平化和完全连接层。模型中使用了ReLU激活函数和Adam优化器。ReliefF是Relief的一种改进的特征选择算法，它使用曼哈顿距离代替欧几里德距离。基于特征的意义,ReliefF将权重分配给从全连接层接收的每个提取特征。每个特征的权重是多类问题中相邻实例对的每个类中k个最接近命中和未命中的平均值。ReliefF通过将节点值设置为零来消除较低权重的功能。保持特征的较高权重以获得特征选择。在神经网络的最后一层，多类支持向量机(SVM)用于对COVID-19、病毒性肺炎和健康病例的模式进行分类。具有三个二进制SVM分类器的三个类按照一对全方法对每个二进制SVM使用线性核函数。选择铰链损失函数和L2范数正则化以获得更稳定的结果。所提出的方法是在Kaggle和GitHub的公开可用的胸部X射线和CT扫描图像数据库上进行评估的。所提出的分类模型的性能具有可比的训练，验证,和测试精度，除了敏感性，特异性，和混淆矩阵，用于五重交叉验证的定量评估。
■我们提出的网络在2级X射线和CT上实现了98.48％和95.34％的测试精度。更重要的是,所提出的模型的测试精度，灵敏度,特异性为87.9%，86.32%，3类分类为90.25%(COVID-19，肺炎，正常）胸部X光片。所提出的模型提供了测试精度，灵敏度,特异性为95.34%，94.12%,胸部CT2类分类（COVID-19，非COVID）占96.15%。
■我们提出的分类网络实验结果表明与现有神经网络的竞争力。所提出的神经网络帮助临床医生确定和监测疾病。
■新增强的混合分类网络（SVM-RLF-DNN）包括三个阶段：特征提取，选择和分类。从一系列3×3卷积中提取深度特征，2×2最大轮询操作，然后是深度神经网络(DNN)的扁平化和完全连接层。模型中使用了ReLU激活函数和Adam优化器。ReliefF是Relief的一种改进的特征选择算法，它使用曼哈顿距离代替欧几里德距离。基于特征的意义,ReliefF将权重分配给从全连接层接收的每个提取特征。每个特征的权重是多类问题中相邻实例对的每个类中k个最接近命中和未命中的平均值。ReliefF通过将节点值设置为零来消除较低权重的功能。保持特征的较高权重以获得特征选择。在神经网络的最后一层，多类支持向量机(SVM)用于对COVID-19、病毒性肺炎和健康病例的模式进行分类。具有三个二进制SVM分类器的三个类按照一对全方法对每个二进制SVM使用线性核函数。选择铰链损失函数和L2范数正则化以获得更稳定的结果。所提出的方法是在Kaggle和GitHub的公开可用的胸部X射线和CT扫描图像数据库上进行评估的。所提出的分类模型的性能具有可比的训练，验证,和测试精度，除了敏感性，特异性，和混淆矩阵，用于五重交叉验证的定量评估。
■我们提出的网络在2级X射线和CT上实现了98.48％和95.34％的测试精度。更重要的是,所提出的模型的测试精度，灵敏度,特异性为87.9%，86.32%，3类分类为90.25%(COVID-19，肺炎，正常）胸部X光片。所提出的模型提供了测试精度，灵敏度,特异性为95.34%，94.12%,胸部CT2类分类（COVID-19，非COVID）占96.15%。
■我们提出的分类网络实验结果表明与现有神经网络的竞争力。所提出的神经网络帮助临床医生确定和监测疾病。
