Predictive Analysis of COVID-19 Symptoms with CXR Imaging and Optimize the X-Ray Imaging Using Segmentation Thresholding Algorithm-An Evolutionary Approach for Bio-Medical Diagnosis
S. Anandha Prasad,
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.
Cited References Fulltext