Fractional-Order Edge Detection Masks for Diabetic Retinopathy Diagnosis as a Case Study

Ismail, Sikiru Oluwarotimi; Said, Lobna A.; Madian, Ahmed H.; Radwan, Ahmed G.

doi:10.3390/computers10030030

Cited by 14 publications

(4 citation statements)

References 45 publications

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“…Retinopathy Diagnosis: Case Studies To accomplish these methods one should be in online mode to save time which is necessary to raise the number of persons registered to a large extent. In [87], the authors discussed fractional-order flters for edge identifcation in biomedical imaging technologies. Te study employed left-sided and right-sidedfractional-order mask flters to fne-tune the image with whole edges detected without noisy data and the performance evaluation of fundus images on the STARE dataset.…”

Section: Deep Learning Algorithms For Diabeticmentioning

confidence: 99%

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Uppamma

Bhattacharya

2023

Journal of Healthcare Engineering

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Diabetic retinopathy (DR) is a common eye retinal disease that is widely spread all over the world. It leads to the complete loss of vision based on the level of severity. It damages both retinal blood vessels and the eye’s microscopic interior layers. To avoid such issues, early detection of DR is essential in association with routine screening methods to discover mild causes in manual initiation. But these diagnostic procedures are extremely difficult and expensive. The unique contributions of the study include the following: first, providing detailed background of the DR disease and the traditional detection techniques. Second, the various imaging techniques and deep learning applications in DR are presented. Third, the different use cases and real-life scenarios are explored relevant to DR detection wherein deep learning techniques have been implemented. The study finally highlights the potential research opportunities for researchers to explore and deliver effective performance results in diabetic retinopathy detection.

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Section: Deep Learning Algorithms For Diabeticmentioning

confidence: 99%

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Uppamma

Bhattacharya

2023

Journal of Healthcare Engineering

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“…For example, the frequency response of FIR and IIR filters can be approximated to the frequency response of the fractional operator. These methods can be used to implement the fractional order operator needed for different systems, such as image processing [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

High Performance Fractional Anisotropic Diffusion Filter forPortable Applications

AbdAlRahman¹,

Al-Atabany²,

Soltan³

et al. 2023

Preprint

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Anisotropic diffusion is one of the most effective methods used in image processing. It can be used to eliminate the small textures of an imagewhile preserving its significant edges. In this paper,a new anisotropic diffusion filter is proposed based on a fractional calculus kernel rather than an integer kernel to improve the overall performance of the filter. Integer and fractional anisotropic filters are implementedusing the Genesys-2 FPGA kit to utilize the efficiency of parallelism in FPGAs. Integer and fractional anisotropic filters are tested against the achievable PSNR value vs the number of iterations. The proposed fractional anisotropic filter has a better PSNR value using a smaller number of iterations, reducing the power and area compared to integer anisotropic filter.The proposed filter can be used in image smoothing,edge detection, image segmentation, image denoisingand cartooning. In addition, the proposed filter will save the power of applications with power constraints, such as portable devices, as it requires fewer iterations than integer anisotropic filters. The proposed filter reduces the required power consumption by 58.2%.

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“…The experimental analysis proved that the proposed methods can suppress efficiently undesirable effects caused by traditional techniques. In [22], two novel central fractional-order masks were proposed to evaluate upon applying salt and pepper noise and Gaussian noise. The fractional-order parameter gives an extra degree of freedom in designing different masks.…”

Section: Introductionmentioning

confidence: 99%

Asumu Fractional Derivative Applied to Edge Detection on SARS-COV2 Images

Nchama

Alfonso

Morales

et al. 2022

Journal of Applied Mathematics

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Edge detection consists of a set of mathematical methods which identifies the points in a digital image where image brightness changes sharply. In the traditional edge detection methods such as the first-order derivative filters, it is easy to lose image information details and the second-order derivative filters are more sensitive to noise. To overcome these problems, the methods based on the fractional differential-order filters have been proposed in the literature. This paper presents the construction and implementation of the Prewitt fractional differential filter in the Asumu definition sense for SARS-COV2 image edge detection. The experiments show that these filters can avoid noise and detect rich edge details. The experimental comparison show that the proposed method outperforms some edge detection methods. In the next paper, we are planning to improve and combine the proposed filters with artificial intelligence algorithm in order to program a training system for SARS-COV2 image classification with the aim of having a supplemental medical diagnostic.

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Fractional-Order Edge Detection Masks for Diabetic Retinopathy Diagnosis as a Case Study

Cited by 14 publications

References 45 publications

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends

High Performance Fractional Anisotropic Diffusion Filter forPortable Applications

Asumu Fractional Derivative Applied to Edge Detection on SARS-COV2 Images

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