A Fast Multiresolution Approach Useful for Retinal Image Segmentation

Castro, Dario Lo; Tegolo, Domenico; Valenti, Cesare

doi:10.5220/0006579203400345

Cited by 3 publications

(6 citation statements)

References 25 publications

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“…The study conducted by Castro and colleagues [6] on diabetic retinopathy demonstrates some noteworthy strengths that make a substantial contribution to the discipline. This work is notable for its careful and precise use of segmentation approaches, particularly the Taurus Wavelet algorithm.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 98%

“…Diabetic retinopathy is a major worldwide health issue that requires the use of sophisticated imaging and segmentation methods to ensure accurate diagnosis and treatment. The Taurus Wavelet Algorithm, introduced by Castro et al in 2018 [6], provides a significant advancement in the field of retinal image segmentation. It effectively addresses the requirement for efficient and automated techniques in analyzing fundus oculi.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In order to examine the complexities of the Taurus Wavelet Algorithm and its noteworthy contributions, we direct our focus towards the research conducted by Castro et al in 2018 [6]. The authors conduct a thorough examination of the objectives, technique, and importance of the Taurus Wavelet Algorithm in their paper titled "A Fast-Multiresolution Approach Useful for Retinal Image Segmentation."…”

Section: Literature Reviewmentioning

confidence: 99%

“…The authors conduct a thorough examination of the objectives, technique, and importance of the Taurus Wavelet Algorithm in their paper titled "A Fast-Multiresolution Approach Useful for Retinal Image Segmentation." Castro and colleagues [6] demonstrated the algorithm's ability to accurately identify important characteristics related to retinal illnesses by comparing it to well-known databases like DRIVE and DIARETDB03. Let us explore their findings in depth, uncovering the potential of the Taurus Wavelet Algorithm in furthering the field of diabetic retinopathy research.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The study conducted by Castro and his colleagues [6] on diabetic retinopathy surely provides significant insights to the profession. However, it does not address particular gaps and deficiencies in the existing knowledge.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

See 4 more Smart Citations

A Systematic Review of Current Scenario of Diabetic Retinopathy Severity Grading using Image Content

Vishwajit B. Gaikwad

2023

IJRITCC

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Review of Current Scenario of Diabetic Retinopathy Severity Grading using Image Content

Vishwajit B. Gaikwad

2023

IJRITCC

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Retinal image synthesis through the least action principle

Castro

Valenti

Tegolo

2020

2020 5th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

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An Approach to Classify and Segment Diabetic Retinopathy and Retinopathy of Prematurity

Krishna¹,

Rao²

2023

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In recent years, retinal disorders have grown to be a serious public health issue. Retinopathy of Prematurity (ROP) and Diabetic Retinopathy (DR) are the foremost factors of vision impairments in children and youngsters correspondingly. These illnesses develop gradually and have no visible symptoms. To avoid vision damage, it is crucial to identify these conditions quickly and receive the appropriate medication. Therefore, a completely automated approach for identifying retinal disorders is needed. It is designed to reduce human contact for the identification of Diabetic Retinopathy (DR) and Retinopathy of Prematurity (ROP) while maintaining the excellent accuracy of the classification. This paper presents an enhanced deep learning model LeNet-5 for retinal disease categorization framework. To achieve the desired findings, the DeepLabv3+ based blood vessel segmentation is carried out. After segmenting the retinal vessels, the features relevant to DR and ROP are extracted using dual channel based Capsule Network (CapsNet). After that, LeNet-5 receives the CapsNet feature map for categorization. To increase the deep learning classifier's performance, the Deep Convolutional Generative Adversarial Network (DCGAN) based data augmentation technique is implemented. The system evaluated in MESSIDOR and private datasets obtained 99.29% and 99.12% accuracy for DR and ROP classification. When the attained results are compared with other existing techniques, it is seen that more successful findings are achieved.

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A Fast Multiresolution Approach Useful for Retinal Image Segmentation

Cited by 3 publications

References 25 publications

A Systematic Review of Current Scenario of Diabetic Retinopathy Severity Grading using Image Content

A Systematic Review of Current Scenario of Diabetic Retinopathy Severity Grading using Image Content

Retinal image synthesis through the least action principle

An Approach to Classify and Segment Diabetic Retinopathy and Retinopathy of Prematurity

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