Detecting COVID-19 from medical images is a challenging task that has excited scientists around the world. COVID-19 started in China in 2019, and it is still spreading even now. Chest X-ray and Computed Tomography (CT) scan are the most important imaging techniques for diagnosing COVID-19. All researchers are looking for effective solutions and fast treatment methods for this epidemic. To reduce the need for medical experts, fast and accurate automated detection techniques are introduced. Deep learning convolution neural network (DL-CNN) technologies are showing remarkable results for detecting cases of COVID-19. In this paper, deep feature concatenation (DFC) mechanism is utilized in two different ways. In the first one, DFC links deep features extracted from X-ray and CT scan using a simple proposed CNN. The other way depends on DFC to combine features extracted from either X-ray or CT scan using the proposed CNN architecture and two modern pre-trained CNNs: ResNet and GoogleNet. The DFC mechanism is applied to form a definitive classification descriptor. The proposed CNN architecture consists of three deep layers to overcome the problem of large time consumption. For each image type, the proposed CNN performance is studied using different optimization algorithms and different values for the maximum number of epochs, the learning rate (LR), and mini-batch (M-B) size. Experiments have demonstrated the superiority of the proposed approach compared to other modern and state-of-the-art methodologies in terms of accuracy, precision, recall and f_score.

No abstract

Coronavirus (COVID-19) is considered as a viral disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Spreading of COVID-19 will continue to affect health and economics. Chest X-ray and CT imaging techniques are crucial for infected patients in the battle with COVID-19. Recently, Convolutional Neural Network has been considered as a type of deep learning tools, and it can be used for detecting diseases such as COVID-19. This paper introduces an efficient architecture for COVID-19 diagnosis from an X-ray dataset. The proposed architecture starts with image pre-processing using lung segmentation and image resizing. Deep feature extraction is performed using the proposed CNN model and different pre-trained models. The classification process is performed using either a Support Vector Machine (SVM) or a Softmax classifier. Simulation results prove that the proposed model can classify COVID-19 images with high accuracies of 98.7% and 98.5% for SVM and Softmax classifiers, respectively. The performance metrics are the processing time,

Corona Virus Disease 19 (COVID-19) firstly spread in China since December 2019. Then, it spread at a high rate around the world. Therefore, rapid diagnosis of COVID-19 has become a very hot research topic. One of the possible diagnostic tools is to use a deep convolution neural network (DCNN) to classify patient images. Chest X-ray is one of the most widely-used imaging techniques for classifying COVID-19 cases. This paper presents a proposed wireless communication and classification system for X-ray images to detect COVID-19 cases. Different modulation techniques are compared to select the most reliable one with less required bandwidth. The proposed DCNN architecture consists of deep feature extraction and classification layers. Firstly, the proposed DCNN hyper-parameters are adjusted in the training phase. Then, the tuned hyper-parameters are utilized in the testing phase. These hyper-parameters are the optimization algorithm, the learning rate, the mini-batch size and the number of epochs. From simulation results, the proposed scheme outperforms other related pre-trained networks. The performance metrics are accuracy, loss, confusion matrix, sensitivity, precision, score, specificity, Receiver Operating Characteristic (ROC) curve, and Area Under the Curve (AUC). The proposed scheme achieves a high accuracy of 97.8 %, a specificity of 98.5 %, and an AUC of 98.9 %.

No abstract