Densely Connected Convolutional Networks (DenseNet) for Diagnosing Coronavirus Disease (COVID-19) from Chest X-ray Imaging

Abstract

Since the beginning of the coronavirus disease (COVID-19) pandemic several machine learning and deep learning methods had been introduced to detect the infected patients using the X-Ray or CT scan images. Numerous sophisticated data-driven methods had been introduced to improve the performance and the accuracy of the diagnosis models. This paper proposes an improved densely connected convolutional networks (DenseNet) method based on transfer learning (TL) to enhance the model performance. The results show promising model accuracy.

COVID-RDNet: A novel coronavirus pneumonia classification model by the mixed dataset

“…However, due to the improper size of the convolution kernel in the resnet model, the problem of feature loss occurs. To overcome the problem, Tabrizchi et al [35] and Zhang YuDong et al [36]…”

“…It alleviates the network degradation problem extent by l reusing local features and adding dimension. The DenseNet model [35][36][37] can effectively enhance the combination of COVID-19 information by connecting local and abstract features. Besides, the network can extract more features on relatively fewer data.…”

“…At present, the existing deep learning models for negative and positive COVID-19 classification include alexnet [21][22][23][24], vgg [25][26][27][28], resnet [29][30][31][32][33], and densenet [34][35][36][37]. Here, Maghdid et al [22] combine the alexNet model with transfer learning, which can effectively classify negative and positive COVID-19 on the mixed X-ray and CT images.…”

“…To solve this problem, the densenet model based on the Dense blocks connects the non-linear extracted information by comprehensively using the shallow features. Tabrizchi et al [35] and Zhang YuDong et al [36] improve the densenet model and transfer learning to improve the classification accuracy of COVID-19. Chowdhury et al [37] use the pre-trained deep learning model to automatically detect COVID-19 from chest X-ray images.…”

A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids

“…For identifying pneumonia, the ensemble classifier using support vector machines with radial basis functions and logistic regression classifiers performs well [58]. To detect coronavirus disease from chest X-ray imaging, an improved densely connected convolutional network method based on transfer learning can be used [59,60]. The efficiency of classifying the eight main personality qualities from text using integration of CNNs and Long Short-Term Memory.…”