ABSTRACT
Accurate diagnostic system is significantly important for timely COVID-19 identification. Diagnosing COVID-19 from chest x-ray images employing the CNN model is recommended for accurate recognition of COVID-19. The existing diagnosis techniques of COVID-19 still lack high accuracy. To handle this problem in this work, we have proposed accurate detection method for COVID-19. In the proposed method, a CNN is incorporated for the diagnosis of COVID-19 using chest x-ray images data. The experimental results illustrate that our technique is good for COVID-19 accurate diagnosis and can be easily implemented in health care systems. © 2021 IEEE.
ABSTRACT
The current epidemic situation due to COVID-19 is a public health disaster worldwide. Forecasting play's, a crucial role in determining the pandemic's hypothetical situation and economic situation. It provides the base for authorities, public health officials, management teams, and other stakeholders to plan for future preventive actions in their companies, citizens, and governments. This paper proposes Auto-Regressive Integrated Moving Average mathematical modeling in integration with Box-Jenkins' model-building approach examining the variation in pandemic severity through the Loess smoothed curves to forecast the COVID-19 pandemic situation. The time-plot and forecasting results show Chinese resilience to pact with pandemic situation effectively whereas India was severely affected by the pandemic. The future forecast for India shows the worst situation by the end of 2021. Pakistan and Bangladesh are the least affected among the specified countries while decline in weekly death cases has been observed in Iran till the end of 2021. We observed the Case Fatality Ratio (CFR) of 2.08% globally. © 2021 IEEE.
ABSTRACT
With the rapid spread of the novel COVID-19 virus, there is an increasing demand for screening COVID-19 patients. Typical methods for screening coronavirus patients have a large false detection rate. An effective and reliable screening method for detecting coronavirus is required. For this reason, some other reliable methods such as Computed Tomography (CT) imaging is employed to detect coronavirus accurately. In this paper, we present a 3D-Deep learning based method that automatically screens coronavirus patients using 3D volumetric CT image data. Our proposed system assists medical practitioners to effectively screen out COVID-19 patients. We performed extensive experiments on two datasets i.e., CC-19 and COVID-CT using various state-of-the-art 3D Deep learning based methods including 3D ResNets, C3D, 3D DenseNets, I3D, and LRCN. The results of the experiments show the competitive effectiveness of our proposed approach.