Fractal analysis related to tumour growth in lungs: A review

Chakravarty, Sandipan Prasad; Chakraborty, Sarbani

doi:10.1109/iciip.2017.8313752

Cited by 3 publications

(1 citation statement)

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“…They explained that the two sets of fractal dimensions of melanoma and non-melanoma moles were not statistically different. Chakravarty and Chakraborty (2017) studied fractal analysis related to tumor growth in human lungs using Computer Tomography (CT) scan images. They explained that to obtain a result with any variance on the number of pixels of an image, the fractal dimension values are changed.…”

Section: Related Workmentioning

confidence: 99%

The Application of Fractal Analysis on Thyroid Ultrasound Images

Bayrak¹,

Kirci²

2019

ACIN

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The concept of fractals is used for complex and irregular objects or structures in systems. The complexity of medical images allows us to use Fractal geometry rather then Euclidian geometry for their characterization. In this paper, we have discussed the analysis of thyroid ultrasound images to calculate fractal dimension. Fractal analysis software was used for the calculation of fractal dimension and correlation coefficient values. In the fractal analysis on the thyroid ultrasound images, the determination of the dimensions of the lesions, nodules or cysts in the region by using fractal concepts can be predicted and used as an assistive system for physicians in the treatment and early diagnosis of thyroid disorders.

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Section: Related Workmentioning

confidence: 99%

The Application of Fractal Analysis on Thyroid Ultrasound Images

Bayrak¹,

Kirci²

2019

ACIN

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Multifractality Analysis of Respiratory Signals

Saatci

Saatçı

2020

2020 28th Signal Processing and Communications Applications Conference (SIU)

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A Novel Transformer Method Pretrained With Masked Autoencoders and Fractal Dimension for Diabetic Retinopathy Classification

YANG,

ZHA,

ZHOU

et al. 2024

Fractals

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Diabetic retinopathy (DR) is one of the leading causes of blindness in a significant portion of the working population, and its damage on vision is irreversible. Therefore, rapid diagnosis on DR is crucial for saving the patient’s eyesight. Since Transformer shows superior performance in the field of computer vision compared with Convolutional Neural Networks (CNNs), it has been proposed and applied in computer aided diagnosis of DR. However, a large number of images should be used for training due to the lack of inductive bias in Transformers. It has been demonstrated that the retinal vessels follow self-similar fractal scaling law, and the fractal dimension of DR patients shows an evident difference from that of normal people. Based on this, the fractal dimension is introduced as a prior into Transformers to mitigate the adverse influence of lack of inductive bias on model performance. A new Transformer method pretrained with Masked Autoencoders and fractal dimension (MAEFD) is developed and proposed in this paper. The experiments on the APTOS dataset show that the classification performance for DR by the proposed MAEFD can be substantially improved. Additionally, the present model pretrained with 100,000 retinal images outperforms that pretrained with 1 million natural images in terms of DR classification performance.

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Fractal analysis related to tumour growth in lungs: A review

Cited by 3 publications

References 0 publications

The Application of Fractal Analysis on Thyroid Ultrasound Images

The Application of Fractal Analysis on Thyroid Ultrasound Images

Multifractality Analysis of Respiratory Signals

A Novel Transformer Method Pretrained With Masked Autoencoders and Fractal Dimension for Diabetic Retinopathy Classification

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