Effective blood vessels reconstruction methodology for early detection and classification of diabetic retinopathy using OCTA images by artificial neural network

Abdelsalam, Mohamed M.

doi:10.1016/j.imu.2020.100390

Cited by 23 publications

(18 citation statements)

References 43 publications

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“…Other global thresholding methods are based on finding a specific percentile of the image intensity histogram [24], the progressive weighted mean of the image intensity histogram [25,26], or by simply fine-tuning a specific gray level [27]. Many analyzed studies employed a global thresholding technique without specifying exactly how the final threshold was determined [22,[28][29][30][31][32][33][34].…”

Section: Thresholdingmentioning

confidence: 99%

“…This classifier was used for single disease detection, such as DR [70,84] and glaucoma [24,29], and was also employed for more complex classification tasks, such as DR staging [33] and distinguishing between different retinopathies [42]. The other classifiers that were used were NNs [32,83,86], k-means clustering [42], logistic regression [84], and a gradient boosting tree (XGBoost) [84].…”

Section: Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

et al. 2021

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Optical coherence tomography angiography (OCTA) is a promising technology for the non-invasive imaging of vasculature. Many studies in literature present automated algorithms to quantify OCTA images, but there is a lack of a review on the most common methods and their comparison considering multiple clinical applications (e.g., ophthalmology and dermatology). Here, we aim to provide readers with a useful review and handbook for automatic segmentation and classification methods using OCTA images, presenting a comparison of techniques found in the literature based on the adopted segmentation or classification method and on the clinical application. Another goal of this study is to provide insight into the direction of research in automated OCTA image analysis, especially in the current era of deep learning.

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Section: Thresholdingmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

et al. 2021

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“…To classify the image of a person with and without diabetic retinopathy an automatic artificial neural network classifier was experimented to provide robust and accurate disease prediction without any over fitting conflict. 16 In order to specifically enhance the classification accuracy and to reveal the degree of diabetic severity, a transformed fuzzy neural network classifier was introduced by extracting the various association rules among the refined…”

Section: Related Workmentioning

confidence: 99%

Cloud‐based onboard prediction and diagnosis of diabetic retinopathy

Sundharamurthy¹,

Kaliappan

2021

Concurrency and Computation

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Diabetic retinopathy remains the most daunting microvascular complication of diabetic disease. Major risk factors of diabetic retinopathy include long-term diabetics, improper blood glucose level, and dyslipidemia. In order to investigate the presence of diabetic retinopathy-related features and to deal with the diagnosis of various diabetic severity stages, there is a need for a cloud-based automated screening system. So, the development of novel biomarkers should take the advantage of objective measurement and evaluation indicating the biological and pathogenic processes to all the therapeutic interventions. Current state-of-the-art research studies lack accommodating large sample size, cross-validation among the diverse populations, time, and cost-effective diagnosis techniques. To overcome these challenges, a cloud-based diabetic retinopathy prediction system is proposed using the recurrent convolution neural network classifier model. The proposed prediction system can provide risk stratification, optimized resource allocation, a severe form of disease prediction, risk control, and preventive interventions during the screening and diagnosis process. It also provides prognostic information to patients stating various cost-effective predictive responses according to treatment modalities. Moreover, the proposed system clinical decision-making will outperform the existing classifiers in terms of prediction accuracy, sensitivity, specificity, precision, recall, and F1-score.

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“…Abdelsalam, 2020 [71] described AI methods which can accurately classify fundus OCTA in diabetic patients to distinguish DR with or without NPDR with a sensitivity and specificity of >96% in a small sample. The methodology uses an artificial neural network (ANN) as an automatic classifier to distinguish between normal subjects without diabetes (n = 40), diabetics without DR (n = 30), and mild to moderate NPDR subjects (n = 30) [71].…”

Section: Ai Approachesmentioning

confidence: 99%

Developments in Non-Invasive Imaging to Guide Diagnosis and Treatment of Proliferative Diabetic Retinopathy: A Systematic Review

Bowditch

Chang

Mehta

2021

IJTM

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Diagnosis and management of proliferative diabetic retinopathy are reliant upon retinal imaging. A systematic literature review of non-invasive imaging to guide diagnosis and treatment of proliferative diabetic retinopathy was performed. There is a trend of moving away from invasive (e.g., fundus fluorescein angiography) to non-invasive (e.g., wide-field optical coherence tomography (OCT), OCT angiography and colour fundus photography) imaging modalities to allow for more objective assessments that can be readily repeated in a time-efficient manner without compromising patient safety. Such quantitative assessments generating large amounts of data could benefit from artificial intelligence approaches to aid clinical decision making. These non-invasive imaging modalities continue to improve both in terms of the quality of image acquisition and progress in image interpretation. It is important that newer non-invasive imaging modalities are appropriately validated in large-scale prospective observational studies or randomised clinical trials.

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Effective blood vessels reconstruction methodology for early detection and classification of diabetic retinopathy using OCTA images by artificial neural network

Cited by 23 publications

References 43 publications

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook

Cloud‐based onboard prediction and diagnosis of diabetic retinopathy

Developments in Non-Invasive Imaging to Guide Diagnosis and Treatment of Proliferative Diabetic Retinopathy: A Systematic Review

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