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

Abstract

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.

Dynamics of the COVID-19 pandemic: nonlinear approaches on the modelling, prediction and control

“…The authors conclude that the economic growth of those markets will recover when the impact of the COVID-19 virus begins to fade. Thangaraj and Easwaramoorthy [35] present a different characterization of the disease and its severity by comparing denoised and noisy computed tomography (CT) scan images collected from COVID-19infected patients, implementing the theory of multifractals. Multifractal measures are examined, and the CT scan images are classified and investigated by means of filtered and edge detection methods.…”

“…This special issue is a compilation of original research articles that address the dynamics and applications of COVID-19 through nonlinear dynamics. The articles are organized in five sections, comprising mathematical modeling and epidemics [1][2][3][4][5][6][7], the dynamics of several waves and transmission [8][9][10][11][12][13][14][15][16][17], neural network and deep learning related to COVID-19 [18][19][20][21][22][23][24], predictions and estimations related to COVID-19 [25][26][27][28][29][30], and detailed analysis on the pandemic and its applications [31][32][33][34][35].…”

Analysis of HBV and COVID‐19 Coinfection Model with Intervention Strategies

“…Literatures of some scholars mentioned in references [ 1 , 3 , 14 – 16 , 28 – 32 ] investigated that COVID-19 disease highly affected individuals already infected with either of HIV or TB or HBV or cholera. Literatures studied by some scholars mentioned in references [ 2 , 4 , 6 , 8 , 13 , 14 , 16 – 18 , 20 – 22 , 24 – 27 , 29 , 32 – 50 ] constructed and examined spreading and control of communicable disease ordinary differential method; similarly, scholars mentioned in [ 3 , 28 , 51 – 53 ] constructed and examined fractional derivative model of infectious diseases, and scholar mentioned in [ 1 ] constructed and examined a stochastic model of an infectious disease.…”

“…Teklu and Terefe [45] analyze COVID-19 and syphilis codynamics model to investigate the impacts of intervention measures on the disease transmission. Thangaraj and Easwaramoorthy [53] investigated a generalized fractal dimension-based comparison of edge detection methods in CT images for estimating the infection of COVID-19 disease. Some epidemiological and medical studies proved that hepatitis B virus and COVID-19 coinfection is a common public health issue.…”

Numerical integration of bivariate fractal interpolation functions on rectangular domains

“…The theory of fractal interpolation has been used in a variety of applications such as curve fitting [ 18 ], image compression [ 7 , 11 , 26 ], tumor perfusion [ 9 ] and Covid-19 data analysis [ 2 , 28 ]. Since most naturally occurring signals have sudden peaks and valleys in their structure, the Euclidean measurements of specific parameters such as length, area, and perimeter become irrelevant.…”