Multifractal based image processing for estimating the complexity of COVID-19 dynamics

Rong, Qiusheng; Thangaraj, C.; Easwaramoorthy, D.; He, Shaobo

doi:10.1140/epjs/s11734-021-00336-1

Cited by 5 publications

(2 citation statements)

References 30 publications

(28 reference statements)

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“…However, depending on the clinical problem and disease, the starting set of features can be different. For instance, if we are planning to quantify the shape of a lesion, then volume and shape-based features such as fractals [ 77 ]. In case, lesion edge and gradients are the region where differential characteristics of less and more aggressive lesions are observed, then we might start off with edge-based features [ 78 ].…”

Section: Introductionmentioning

confidence: 99%

Ten quick tips for computational analysis of medical images

Chicco

Shiradkar

2023

PLoS Comput Biol

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Medical imaging is a great asset for modern medicine, since it allows physicians to spatially interrogate a disease site, resulting in precise intervention for diagnosis and treatment, and to observe particular aspect of patients’ conditions that otherwise would not be noticeable. Computational analysis of medical images, moreover, can allow the discovery of disease patterns and correlations among cohorts of patients with the same disease, thus suggesting common causes or providing useful information for better therapies and cures. Machine learning and deep learning applied to medical images, in particular, have produced new, unprecedented results that can pave the way to advanced frontiers of medical discoveries. While computational analysis of medical images has become easier, however, the possibility to make mistakes or generate inflated or misleading results has become easier, too, hindering reproducibility and deployment. In this article, we provide ten quick tips to perform computational analysis of medical images avoiding common mistakes and pitfalls that we noticed in multiple studies in the past. We believe our ten guidelines, if taken into practice, can help the computational–medical imaging community to perform better scientific research that eventually can have a positive impact on the lives of patients worldwide.

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Section: Introductionmentioning

confidence: 99%

Ten quick tips for computational analysis of medical images

Chicco

Shiradkar

2023

PLoS Comput Biol

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“…In [ 34 ], the transmission of COVID-19 is analyzed using Generalized Fractal Dimensions. X-ray images from COVID-19 patients and healthy individuals are compared graphically.…”

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confidence: 99%

Frontiers of fractals for complex systems: recent advances and future challenges

Gowrisankar

Banerjee

2021

Eur. Phys. J. Spec. Top.

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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 based image processing for estimating the complexity of COVID-19 dynamics

Cited by 5 publications

References 30 publications

Ten quick tips for computational analysis of medical images

Ten quick tips for computational analysis of medical images

Frontiers of fractals for complex systems: recent advances and future challenges

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

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