Saha Sanjib, Nandi Debashis
Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India.
Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, India.
Digit Health. 2024 May 27;10:20552076241257045. doi: 10.1177/20552076241257045. eCollection 2024 Jan-Dec.
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.
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.
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.
Our proposed classification network experimental results indicate competitiveness with existing neural networks. The proposed neural network assists clinicians in determining and surveilling the disease.
开发一种先进的检测技术，利用深度学习和机器学习方法的独特应用，从胸部X光和CT扫描胶片中检测新冠病毒模式。
新提出的增强型混合分类网络（SVM-RLF-DNN）包括三个阶段：特征提取、选择和分类。深度特征是从一系列3×3卷积、2×2最大池化操作中提取的，随后是深度神经网络（DNN）的展平层和全连接层。模型中使用了ReLU激活函数和Adam优化器。ReliefF是Relief的一种改进特征选择算法，它使用曼哈顿距离而非欧几里得距离。基于特征的重要性，ReliefF为从全连接层接收的每个提取特征分配权重。对于多类问题中的相邻实例对，每个特征的权重是每个类中k个最接近的命中和未命中的平均值。ReliefF通过将节点值设置为零来消除低权重特征。保留较高权重的特征以获得特征选择。在神经网络的最后一层，使用多类支持向量机（SVM）对新冠病毒、病毒性肺炎和健康病例的模式进行分类。三个类别使用三个二元SVM分类器，按照一对多方法，每个二元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。所提出的方法在来自Kaggle和GitHub的公开可用胸部X光和CT扫描图像数据库上进行评估。所提出的分类模型在五折交叉验证的定量评估中，具有可比的训练、验证和测试准确率，以及灵敏度、特异性和混淆矩阵。
我们提出的网络在二类X光和CT上分别实现了98.48%和95.34%的测试准确率。更重要的是，对于胸部X光上的三类分类（新冠病毒、肺炎、正常），所提出模型的测试准确率、灵敏度和特异性分别为87.9%、86.32%和90.25%。对于胸部CT上的二类分类（新冠病毒、非新冠病毒），所提出模型的测试准确率、灵敏度和特异性分别为95.34%、94.12%和96.15%。
我们提出的分类网络实验结果表明与现有神经网络具有竞争力。所提出的神经网络有助于临床医生诊断和监测疾病。
