Fractal Analysis in Pulmonary CT Images of COVID-19-Infected Patients

Author:

Paun Maria-Alexandra12ORCID,Postolache Paraschiva34,Nichita Mihai-Virgil5ORCID,Paun Vladimir-Alexandru6,Paun Viorel-Puiu78

Affiliation:

1. School of Engineering, Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland

2. Section Market Access and Conformity, Division Radio Monitoring and Equipment, Federal Office of Communications (OFCOM), 2501 Bienne, Switzerland

3. Medical Department, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania

4. Medical Department, Rehabilitation Clinical Hospital, 700661 Iasi, Romania

5. Doctoral School, Faculty of Applied Sciences, University Politehnica of Bucharest, 060042 Bucharest, Romania

6. Five Rescue Research Laboratory, 75004 Paris, France

7. Department of Physics, Faculty of Applied Sciences, University Politehnica of Bucharest, 060042 Bucharest, Romania

8. Academy of Romanian Scientists, 050094 Bucharest, Romania

Abstract

In this paper, we propose to quantitatively compare the loss of human lung health under the influence of the illness with COVID-19, based on the fractal-analysis interpretation of the chest-pulmonary CT pictures, in the case of small datasets, which are usually encountered in medical applications. The fractal analysis characteristics, such as fractal dimension and lacunarity measured values, have been utilized as an effective advisor to interpretation of pulmonary CT picture texture.

Publisher

MDPI AG

Subject

Statistics and Probability,Statistical and Nonlinear Physics,Analysis

Reference31 articles.

1. (2023, January 04). Weekly Epidemiological Update on COVID-19. Available online: https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19.

2. COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning;Afshar;Sci. Data,2021

3. Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network;Qi;Int. J. Comput. Assist. Radiol. Surg.,2021

4. Coronavirus disease 2019 (COVID-19): Role of chest CT in diagnosis and management;Li;AJR Am. J. Roentgenol.,2020

5. COVIDNet-CT: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest CT images;Gunraj;Front. Med.,2020

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