Generalized fractal dimensions based comparison analysis of edge detection methods in CT images for estimating the infection of COVID-19 disease

The coronavirus, also known as COVID-19, has become highly contagious and has been associated with one of the world’s deadliest diseases. It also has direct effects on human lungs, causing significant damage. CT-scans are commonly employed in such circumstances to promptly evaluate, detect, and treat COVID-19 patients. Without any filtering, CT-scan images are more difficult to identify the damaged parts of the lungs and determine the severity of various diseases. In this paper, we use the multifractal theory to evaluate COVID-19 patient’s CT-scan images to analyze the complexity of the various patient’s original, filtered, and edge detected CT-scan images. To precisely characterize the severity of the disease, the original, noisy and denoised images are compared. Furthermore, the edge detection and filtered methods called Robert, Prewitt, and Sobel are applied to analyze the various patient’s COVID-19 CT-scan images and examined by the multifractal measure in the proposed technique. All of the images are converted, filtered and edge detected using Robert, Prewitt, and Sobel edge detection algorithms, and compared by the Generalized Fractal Dimensions are compared. For the CT-scan images of COVID-19 patients, the various Qualitative Measures are also computed exactly for the filtered and edge detected images by Robert, Prewitt, and Sobel schemes. It is observed that Sobel method is performed well for classifying the COIVD-19 patients’ CT-scans used in this research study, when compared to other algorithms. Since the image complexity of the Sobel method is very high for all the images and then more complexity of the images contains more clarity to confirm the COVID-19 images. Finally, the proposed method is supported by ANOVA test and box plots, and the same type of classification in experimental images is explored statistically.