COVID-19 prediction with Chest X-Ray images using CNN

Coronavirus has outbreak as an epidemic disease, created a pandemic situation for the public health across the Globe. Screening for the large masses is extremely crucial to control disease for the people in a neighborhood. Real-time-PCR[18] is the general diagnostic approach for pathological examination. However, the increasing figure of false results from the test has created a way in choosing alternative procedures. COVID-19 patient's X-rays images of chest has emerged as a significant approach for screening the COVID-19 disease. However, accuracy depends on the knowledge of a radiologist. X-Ray images of lungs may be proper assistive tool for diagnosis in reducing the burden of the doctor. Deep Learning techniques, especially Convolutional Neural Networks (CNN), have been shown to be effective for classification of images in the medical field. Diagnosing the COVID-19 using the four types of Deep-CNN models because they have pre-trained weights. Model needs to pre-trained on the ImageNet database in simplifying the large datasets. CNN-based architectures were found to be ideal in diagnosing the COVID-19 disease. The model having an efficiency of 0.9835 in accuracy, precision of 0.915, sensitivity of 0.963, specificity with 0.972, 0.987 F1 Score and 0.925 ROC AUC. © 2023 IEEE.

Peripheral lymphocyte subset alteration in patients with COVID-19 having differential clinical manifestations.

Background & objectives: The COVID-19 disease profile in Indian patients has been found to be different from the Western world. Changes in lymphocyte compartment have been correlated with disease course, illness severity and clinical outcome. This study was aimed to assess the peripheral lymphocyte phenotype and subset distribution in patients with COVID-19 disease from India with differential clinical manifestations. Methods: Percentages of peripheral lymphocyte subsets were measured by flow cytometry in hospitalized asymptomatic (n=53), mild symptomatic (n=36), moderate and severe (n=30) patients with SARS-CoV-2 infection, recovered individuals (n=40) and uninfected controls (n=56) from Pune, Maharashtra, India. Results: Percentages of CD4+Th cells were significantly high in asymptomatic, mild symptomatic, moderate and severe patients and recovered individuals compared to controls. Percentages of Th memory (CD3+CD4+CD45RO+), Tc memory (CD3+CD8+CD45RO+) and B memory (CD19+CD27+) cells were significantly higher in the recovered group compared to both asymptomatic, mild symptomatic patient and uninfected control groups. NK cell (CD56+CD3-) percentages were comparable among moderate +severe patient and uninfected control groups. Interpretation & conclusions: The observed lower CD4+Th cells in moderate+severe group requiring oxygen support compared to asymptomatic+mild symptomatic group not requiring oxygen support could be indicative of poor prognosis. Higher Th memory, Tc memory and B memory cells in the recovered group compared to mild symptomatic patient groups might be markers of recovery from mild infection; however, it remains to be established if the persistence of any of these cells could be considered as a correlate of protection.

An Empirical study and assessment of minority oversampling with Dynamic Ensemble Selection on COVID-19 utilizing Blood Sample

The COVID-19 virus disease outbreak that erupted in China at the end of 2019 has had a tremendous and disastrous impact on the rest of the world. It has struck the globe to its core, and the destruction has substantially increased the diagnostic burden. In the pandemic zone, clinicians will be able to cut down on their workload and get the right diagnosis of the new disease great to the use of machine learning. A blood test has emerged as a critical tool for identifying false-positive or false-negative real-time rRT-PCR diagnostics. Notably, this is mostly because it is such a cost-effective and convenient method of detecting probable COVID-19 patients. Among the numerous hard consequences associated with COVID-19 illness has been established as one of the most prevalent among COVID-19 patients. The impetus for this research is the scarcity of post-COVID-19 dataset. Following pre-processing to manage address missing values, oversampling with SMOTE ENN is used to generate several instances and model training is carried out on these data sets. However, it has been demonstrated that normatively dynamic ensemble selection outperforms static selection and dynamic selection. The DI+SMOTEENN+DESKNU exceed existing benchmark Classification algorithms and obtain the best accuracy of 99.6%, according the results. © 2022 IEEE.

Drone-Based Social Distancing, Sanitization, Inspection, Monitoring, and Control Room for COVID-19

In recent times, the COVID-19 pandemic has affected billions of people worldwide and has resulted in the slowing down of the economy, industry shutdown, job losses, etc. Every country has taken appropriate measures to fight against pandemic by keeping in mind that health is the primary concern for human beings. This work introduces the COVID-19 pandemic and discusses its types, influence over mankind, prevention methods, and latest observations. Further, this study has designed drone-based case studies for pandemic monitoring, social distance measurements, the necessity of the control room, etc. The simulation is designed to have a single-layer drone movement strategy with a fixed distance. The simulation experimentation is derived from real-time drone movement and area coverage for sanitization. The drone movement and collision avoidance strategy are pre-emptive in nature, i.e., drones are derived to move to a fixed location and execute its functionality. At the ground level, service is designed for which people make queues and maintain social distance before being served. This case study shows its successful execution and can be mapped to a real-time environment. Further, a case study is extended to observe the real-time ambulance monitoring for patient pickup and drop at the hospital. Results show its successful working and continuous operation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.