Multifractal analysis on age-based discrimination in X-ray images for sensing the severity of COVID-19 disease.

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.