ABSTRACT
Background: Severe Acute Respiratory Syndrome Corona Virus Disease 2019 (COVID-19) is responsible for the COVID-19 pandemic causing respiratory illness worldwide. Hence there is an urgent need for simple, rapid, and accurate tests for diagnosis. Performance characteristic of Rapid Antigen detection test for identifying sensitivity and specificity with gold standard Real-Time Polymerase Chain Reaction and correlate the significance of non-specific parameters like CRP(C- Reactive Protein), IL-6(Interleukin-6), Procalcitonin for diagnosis of COVID-19. Method(s): The Rapid Antigen Detection test was compared with Real-Time Polymerase Chain Reaction to detect SARS CoV-2 in respiratory specimens. 100 respiratory samples (mainly nasopharyngeal and throat swab) and for CRP, IL6, and Procalcitonin serum samples were obtained from COVID-19 suspected cases, mostly in-patients at Saveetha Medical College from April 2021- to September 2021. Result(s): Out of 100 samples, 67% were positive, 33% were negative for SARS-CoV2 RNA by RT-PCR assay. When compared with Rapid Antigen Test, the RT-PCR test showed 83.8% sensitivity and 59.3% specificity, while non-specific parameters correlation in diagnosis of COVID-19 showed CRP insignificant, IL-6 and Procalcitonin significant. Conclusion(s): RT-PCR is considered the standard gold method for diagnosing COVID-19. On comparing RT-PCR with other non-specific tests like CRP, Procalcitonin, IL-6 showed Procalcitonin and IL-6 can be considered non-specific tests for diagnosing COVID-19. Copyright © 2022 are reserved by International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
ABSTRACT
Background and Objective: The concept of Too Close and Too Short spreads the virus simultaneously from one person to another person. The radiologist investigates to prevent the spreading of the virus in the earlier stage of diagnosis and predict the symptoms in the beginning stage. The objective of the study is to predict the virus in the earlier stage and diagnose it so that the mortality rate is reduced. Hence, the virus-infected person lives a healthier life. Materials and Methods: The Chest X-ray imaging segments the image concerning edge detection (with and without contour detection) for earlier identification and prediction of COVID-19 symptoms. The segmentation thresholding algorithm accurately detects the parameters of fever, pneumonia and mucus fluid in earlier predictions. Moreover, the segmentation thresholding algorithm automatically classifies with the prediction of COVID-19 symptoms in pixel shape, size and intensity. Extraction of image features for pixel size, shape and intensity for feature enhancement and measurement. Results: Validation of segmentation thresholding algorithm improves with high accuracy in Chest X-ray imaging. The predictive analysis of CXR imaging to Accuracy, Precision, F-measure and Recall accurately enhanced with symptoms of COVID-19 in an earlier stage. The future study of the proposed method detection of COVID-19 symptoms is predicted in the earlier stage can be diagnosed automatically. Conclusion: Detection of COVID-19 symptoms in earlier stage processed through CXR imaging via Automatic Segmentation Threshold Algorithm clusters the pixel concerning contour and non-contour edge detection. The accuracy detection of contour and non-contour edge detection extract the image feature 90% of the original enhanced image.