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

He, Shaobo; Thangaraj, C.; Easwaramoorthy, D.; Muhiuddin, G.

doi:10.1140/epjs/s11734-022-00615-5

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…The article by He et al [31] implements the theory of multifractals to illustrate the impact of infections at different age groups by classifying X-ray images. The study is performed at different levels of noise using filtered and normal X-ray images for better understanding of lung infections.…”

Section: Covid-19-related Analysis and Applicationsmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Banerjee

2022

Eur. Phys. J. Spec. Top.

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This special issue contains 35 regular articles on the analysis and dynamics of COVID-19 with several applications. Some analyses are on the construction of mathematical models representing the dynamics of COVID-19, and some are on the estimations and predictions of the disease, a few with possible applications. The various contributions report important, timely, and promising results, such as the effects of several waves, deep learning-based COVID-19 classifications, and multivariate time series with applications.

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Section: Covid-19-related Analysis and Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Banerjee

2022

Eur. Phys. J. Spec. Top.

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“…Fractal lacunarity has been used in [15,17]. Multifractal features (generalized dimensions, singularity spectrum) have provided excellent classification accuracy in [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Structural Characterization of Toxoplasma gondii Brain Cysts in a Model of Reactivated Toxoplasmosis Using Computational Image Analysis

Bauman,

Srbljanović,

Čolović Čalovski

et al. 2024

Fractal Fract

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Toxoplasma gondii is an obligate intracellular parasite existing in three infectious life stages—tachyzoites, bradyzoites, and sporozoites. Rupture of tissue cysts and re-conversion of bradyzoites to tachyzoites leads to reactivated toxoplasmosis (RT) in an immunocompromised host. The aim of this study was to apply ImageJ software for analysis of T. gondii brain cysts obtained from a newly established in vivo model of RT. Mice chronically infected with T. gondii (BGD1 and BGD26 strains) were treated with cyclophosphamide and hydrocortisone (experimental group—EG) or left untreated as infection controls (ICs). RT in mice was confirmed by qPCR (PCR+); mice remaining chronically infected were PCR−. A total of 90 images of cysts were analyzed for fractal dimension (FD), lacunarity (L), diameter (D), circularity (C), and packing density (PD). Circularity was significantly higher in PCR+ compared to IC mice (p < 0.05 for BGD1, p < 0.001 for the BGD26 strain). A significant negative correlation between D and PD was observed only in IC for the BGD1 strain (ρ = −0.384, p = 0.048), while fractal parameters were stable. Significantly higher D, C, and PD and lower lacunarity, L, were noticed in the BGD1 compared to the more aggressive BGD26 strain. In conclusion, these results demonstrate the complexity of structural alterations of T. gondii cysts in an immunocompromised host and emphasize the application potential of ImageJ in the experimental models of toxoplasmosis.

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Fractal based automatic detection of complexity in COVID‐19 X‐ray images

Thangaraj,

Easwaramoorthy,

Muhiuddin

et al. 2023

Expert Systems

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The coronavirus was discovered in Wuhan, China, in December 2019. Scientists and medical practitioners have warned that this fatal virus could spread quickly from person to person in its early stages and its impact will be far more vigorous than the previously discovered viruses. The World Health Organization has given warning propaganda to all nations about this harmful virus. However, the diffusion speed of COVID‐19 was so rapid that it spread to all countries much faster than the researchers predicted, causing the widespread human disaster. The genetic variations of COVID‐19 have also astounded researchers today, as it modifies its mutation with very large genetic strains. The virus is regarded as a human version of the disease, resulting in the common cold, dry cough, and respiratory problems in severe cases. According to health organisations, the coronavirus directly affects the lungs, causing main problems such as difficulty breathing. It is also tough for physicians to diagnose the disease level properly by using the regular process. Even though the virus can be detected in regular testing methods, even computed tomography (CT) and X‐ray images are widely used in the medical field to identify the respiratory problems caused by the COVID‐19 virus. Like COVID‐19, some other types of pneumonia respiratory diseases also affect the human lungs. The fractal dimension (FD) is an interesting non‐linear measure to describe the complexity of visual images. In this context, the complexity of X‐ray images is analysed by using the fractal dimension. The fractal dimension is an excellent non‐linear measurement for describing the complexity of realistic images. The difference between the complexity of the X‐ray images of COVID‐19‐infected patients and the X‐ray images of other types of pneumonia respiratory diseases is well explained by using the fractal dimension. It is also concluded that the fractal dimension discriminates the lung diseases due to COVID‐19 from various pneumonia respiratory diseases.

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

Cited by 4 publications

References 32 publications

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

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

Structural Characterization of Toxoplasma gondii Brain Cysts in a Model of Reactivated Toxoplasmosis Using Computational Image Analysis

Fractal based automatic detection of complexity in COVID‐19 X‐ray images

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