COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images.

The COVID-19 pandemic has a significant negative effect on people's health, as well as on the world's economy. Polymerase chain reaction (PCR) is one of the main tests used to detect COVID-19 infection. However, it is expensive, time-consuming, and lacks sufficient accuracy. In recent years, convolutional neural networks have grabbed many researchers' attention in the machine learning field, due to its high diagnosis accuracy, especially the medical image recognition. Many architectures such as Inception, ResNet, DenseNet, and VGG16 have been proposed and gained an excellent performance at a low computational cost. Moreover, in a way to accelerate the training of these traditional architectures, residual connections are combined with inception architecture. Therefore, many hybrid architectures such as Inception-ResNetV2 are further introduced. This paper proposes an enhanced Inception-ResNetV2 deep learning model that can diagnose chest X-ray (CXR) scans with high accuracy. Besides, a Grad-CAM algorithm is used to enhance the visualization of the infected regions of the lungs in CXR images. Compared with state-of-the-art methods, our proposed paper proves superiority in terms of accuracy, recall, precision, and F1-measure.

Technology-driven 5G enabled e-healthcare system during COVID-19 pandemic

Abstract Technology-driven control measures could be an important tool to control the COVID-19 pandemic crisis. This study evaluates the potentiality of emerging technologies such as 5G and 6G communication, Deep Learning (DL), big data, Internet of Things (IoT) etc. for controlling the COVID-19 transmission and ensuring health safety. The healthcare sector is able to provide a unified, rapid, and incessant service to people by applying modern wireless connectivity tools like 5G or 6G during the COVID-19 pandemic. This study has identified eight key areas of applications for the COVID-19 management like infection detection;travel history analysis;identification of infection symptoms;early detection;transmission identification;access to information in lockdown;movement of people;and development of medical treatments and vaccines. Data have been collected from the respondents living in Sakaka city, KSA during pandemic. This study reveals that most people receive information from social networking sites, health professionals, and television without facing any challenges. The analysis shows that, during the COVID-19 pandemic, about 42% of respondents felt tense always or most of the time in a day. Only 28.6% of respondents felt tense sometimes, whereas the remainder (about 30%) did not feel tense in relation to the COVID-19 crisis. Satisfaction with COVID-19-related information is also positively correlated with COVID-19-related information literacy (r = 0.53, p < 0.01) that is also positively correlated with depression or emotion, anxiety, and stress (r = -0.15, p < 0.05). The long-term pandemic is creating several psychological symptoms including anxiety, stress, and depression, irrespective of age.

An Intelligent Medical Imaging Approach for Various Blood Structure Classifications

Blood is a vital body fluid and can be instrumental in identifying various pathological conditions. Nowadays, a lot of people are suffering from COVID-19 and every country has its own limited testing capacity. Consequently, a system is required to help doctors analyze a patient’s blood structure including COVID-19. Therefore, in this paper, we extracted and selected blood features by proposing a new feature extraction and selection method named stepwise linear discriminant analysis (SWLDA). SWLDA emphasizes on picking confined features from blood structure images and discerning its class based on reversion value such as partial F value. SWLDA begins with picking an equivalence comprising the sole finest X variable and then puts in effort to add more Xs individually, providing the situations are adequate. The process of adding and picking is based on F value to determine which variable would be entered. Then, the picked or the default F-to-enter value is compared with the uppermost partial F value. After this step, the forward addition or backward removal begins and whether the partial test values for all the predictor variables already in the line are estimated is known. Then, the comparison is made between the lowermost partial test value (FL) and preselected or defaulting consequence levels such as F0 (i.e., if F0 > FL, the variable ZL is removed, and the F test is started again;otherwise, the regression equation is adopted). Finally, the system is trained by employing support vector machine (SVM) to label the blood images. The performance of the proposed approach is assessed by employing 8 different datasets of blood structures. It is assured that the proposed method has achieved significant results under different blood structure images including COVID-19.

Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care.

COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing characteristics, and the lack of a vaccine or effective medicines. A model for measuring and preventing the continued spread of COVID-19 is urgently required to provide smart health care services. This requires using advanced intelligent computing such as artificial intelligence, machine learning, deep learning, cognitive computing, cloud computing, fog computing, and edge computing. This paper proposes a model for predicting COVID-19 using the SIR and machine learning for smart health care and the well-being of the citizens of KSA. Knowing the number of susceptible, infected, and recovered cases each day is critical for mathematical modeling to be able to identify the behavioral effects of the pandemic. It forecasts the situation for the upcoming 700 days. The proposed system predicts whether COVID-19 will spread in the population or die out in the long run. Mathematical analysis and simulation results are presented here as a means to forecast the progress of the outbreak and its possible end for three types of scenarios: "no actions," "lockdown," and "new medicines." The effect of interventions like lockdown and new medicines is compared with the "no actions" scenario. The lockdown case delays the peak point by decreasing the infection and affects the area equality rule of the infected curves. On the other side, new medicines have a significant impact on infected curve by decreasing the number of infected people about time. Available forecast data on COVID-19 using simulations predict that the highest level of cases might occur between 15 and 30 November 2020. Simulation data suggest that the virus might be fully under control only after June 2021. The reproductive rate shows that measures such as government lockdowns and isolation of individuals are not enough to stop the pandemic. This study recommends that authorities should, as soon as possible, apply a strict long-term containment strategy to reduce the epidemic size successfully.