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.
ABSTRACT
The coronavirus, also known as COVID-19, which has been considered one of the deadliest diseases in the world, has become highly contagious, it also implants directly in the human lungs and causes severe damage to the lungs. In such case, X-ray images are widely used to analyze, detect and treat the COVID-19 patients quickly. The X-ray images without any filtering are more complex to identify the affected areas of lungs and to estimate the level of severity of various diseases. The paper analyzes the normal and filtered X-ray images through the multifractal theory and describes the effects of the infection on COVID-19 patients at different ages are classified significantly in processed X-ray images. In this study, the mean absolute error and peak signal-to-noise ratio values are calculated for comparing the noisy and denoised X-ray images using the median filter method and analyzed for comparing the severity of lung affection in X-ray images at different noise levels. Finally, the three-dimensional visualization is constructed for representative images for analyzing and comparing the fever and oxygen levels based on the ages using the corresponding Generalized Fractal Dimensions values. It is observed that the Generalized Fractal Dimensions analyze the different sets of age people's X-ray images and shows clearly that the older people have higher complexity and the younger people have lower complexity in the infected lungs.
ABSTRACT
The COVID-19 pandemic creates a worldwide threat to human health, medical practitioners, social structures, and finance sectors. The coronavirus epidemic has a significant impact on people's health, survival, employment, and financial crises; while also having noticeable harmful effects on our environment in a short span of time. In this context, the complexity of the Corona Virus transmission is estimated and analyzed by the measure of non-linearity called the Generalized Fractal Dimensions (GFD) on the chest X-Ray images. Grayscale image is considered as the most important suitable tool in the medical image processing. Particularly, COVID-19 affects the human lungs vigorously within a few days. It is a very challenging task to differentiate the COVID-19 infections from the various respiratory diseases represented in this study. The multifractal dimension measure is calculated for the original, noisy and denoised images to estimate the robustness of COVID-19 and other noticeable diseases. Also the comparison of COVID-19 X-Ray images is performed graphically with the images of healthy and other diseases to state the level of complexity of diseases in terms of GFD curves. In addition, the Mean Absolute Error (MAE) and the Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of the denoising process involved in the proposed comparative analysis of the representative grayscale images.
ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic has fatalized 216 countries across the world and has claimed the lives of millions of people globally. Researches are being carried out worldwide by scientists to understand the nature of this catastrophic virus and find a potential vaccine for it. The most possible efforts have been taken to present this paper as a form of contribution to the understanding of this lethal virus in the first and second wave. This paper presents a unique technique for the methodical comparison of disastrous virus dissemination in two waves amid five most infested countries and the death rate of the virus in order to attain a clear view on the behaviour of the spread of the disease. For this study, the data set of the number of deaths per day and the number of infected cases per day of the most affected countries, the USA, Brazil, Russia, India, and the UK, have been considered in the first and second waves. The correlation fractal dimension has been estimated for the prescribed data sets of COVID-19, and the rate of death has been compared based on the correlation fractal dimension estimate curve. The statistical tool, analysis of variance, has also been used to support the performance of the proposed method. Further, the prediction of the daily death rate has been demonstrated through the autoregressive moving average model. In addition, this study also emphasis a feasible reconstruction of the death rate based on the fractal interpolation function. Subsequently, the normal probability plot is portrayed for the original data and the predicted data, derived through the fractal interpolation function to estimate the accuracy of the prediction. Finally, this paper neatly summarized with the comparison and prediction of epidemic curve of the first and second waves of COVID-19 pandemic to visualize the transmission rate in the both times.
ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic has fatalized 216 countries across the world and has claimed the lives of millions of people globally. Researches are being carried out worldwide by scientists to understand the nature of this catastrophic virus and find a potential vaccine for it. The most possible efforts have been taken to present this paper as a form of contribution to the understanding of this lethal virus in the first and second wave. This paper presents a unique technique for the methodical comparison of disastrous virus dissemination in two waves amid five most infested countries and the death rate of the virus in order to attain a clear view on the behaviour of the spread of the disease. For this study, the dataset of the number of deaths per day and the number of infected cases per day of the most affected countries, The United States of America, Brazil, Russia, India, and The United Kingdom have been considered in first and second wave. The correlation fractal dimension has been estimated for the prescribed datasets of COVID-19 and the rate of death has been compared based on the correlation fractal dimension estimate curve. The statistical tool, analysis of variance has also been used to support the performance of the proposed method. Further, the prediction of the daily death rate has been demonstrated through the autoregressive moving average model. In addition, this study also emphasis a feasible reconstruction of the death rate based on the fractal interpolation function. Subsequently, the normal probability plot is portrayed for the original data and the predicted data, derived through the fractal interpolation function to estimate the accuracy of the prediction. Finally, this paper neatly summarized with the comparison and prediction of epidemic curve of the first and second waves of COVID-19 pandemic to picturize the transmission rate in the both times.