Detecting Pneumonia for COVID 19 Patients using Multi-Image Fusion for CT Images

Early in 2020, the global spread of Coronavirus Disease 2019 (COVID-19) triggered an existential health crisis. Automated lung infection diagnosis using Computed Tomography (CT) images has the potential to significantly improve the current healthcare approach to combat COVID-19. But segmenting infected regions from CT slices is difficult due to the wide variety in infection traits and the weak contrast between infected and healthy tissues. Additionally, gathering a lot of data quickly is impractical, which hinders the training of a deep model. This study proposes COVID-SegNet, a convolutional-based deep learning technique for automatically segmenting COVID-19 infection areas and the whole lungs from chest CT images. The suggested deep CNN includes a feature variation (FV) block that adaptively modifies the global properties of the features for segmenting COVID-19 infection. This can improve its capacity to express features in various situations efficiently and adaptively. To deal with the complex shape variations of COVID-19 infection zones, additionally recommend the use of PASPP, a progressive atrous spatial pyramid pooling. After a simple convolution module, PASPP generates the final features using multistage parallel fusion branches. In order to cover a variety of receptive fields, PASPP uses atrous filters with an acceptable dilation rate in each atrous convolutional layer. For the segmentation of COVID-19 and the lungs, the dice similarity coefficients are 0.987 as well as 0.726, respectively. Experiments carried out on data gathered in the scan centre demonstrate that effectively produce good performance. © 2022 IEEE.

Advanced Deep Learning Model for Future Forecasting of COVID-19

The roll out of corona virus (COVID-19) in the entire every country has put the mankind in danger. The assets of the absolute biggest economies are worried because of the enormous infectivity and contagiousness of this sickness. The capacity of machine learning models to conjecture the quantity and number of impending peoples influenced by corona virus which is by and by took as a possible danger for humankind. Specifically, Three layer of determining models, least outright shrinkage and choice administrator (LASSO) Support vector Machine – deep learning have been utilized in this investigation to estimate the undermining components of corona virus. Minimum Three kinds of expectations are proposed by every one of the systems, like the quantity of recently tainted reports, the quantity of passing’s, and the quantity of recuperations But in the can’t foresee the exact outcome for the patients. To defeat the issue, Proposed strategy utilizing the long Short-term Integrated Average (LSTIA) foresee the quantity of COVID-19 cases in next 30 days ahead and impact of preventive estimates like social segregation and lockdown on the Roll out of corona virus.