A Survey on Deep-Learning-Based Diabetic Retinopathy Classification

Sebastian, Anila; Elharrouss, Omar; Al-Maadeed, Somaya; Almaadeed, Noor

doi:10.3390/diagnostics13030345

Cited by 37 publications

(18 citation statements)

References 115 publications

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“…Early diagnosis through automatic detection utilizing modern technology can help prevent consequences like eyesight loss. DL-based approaches are now heavily preferred for creating DR detectors, mainly due to the advancement of AI technologies, in general, Sebastian et al (2023) ; Mishra et al (2022) . This section discusses major DL concepts related to diabetic DR.…”

Section: Deep Learning In Diabetic Retinopathymentioning

confidence: 99%

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Bhulakshmi,

Rajput

2024

PeerJ Computer Science

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Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

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Section: Deep Learning In Diabetic Retinopathymentioning

confidence: 99%

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Bhulakshmi,

Rajput

2024

PeerJ Computer Science

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“…Damage to the macula can cause a loss of central vision, which can make it difficult to perform tasks such as reading, writing, and recognizing faces. [1] provide a review of work on machine learning as it relates to fundus photography. [2] also covers the literature of machine learning but for a more directed study of diabetic retinopathy severity diagnosis.…”

Section: Eye Anatomy With Respect To Fundus Photographymentioning

confidence: 99%

Quantum Machine Learning for Ocular Disease Recognition

Topaloglu¹

2023

Preprint

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In this paper we use quantum machine learning to detect and classify ocular diseases across age related macular degradation, cataract, diabetic, glaucoma, hypertension, and patological myopia categories versus a control group. We analyze fundus imagery from 1000 patients. Early findings indicate there may be benefit in terms of accuracy and loss function minimization of 2.07% and 1.979x respectively compared to a similar method implemented using traditional computers.

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“…Retinal vasculature segmentation has a significant role in developing such systems. It is usable as a pre-processing/feature extraction step to develop DR detection or DR grading systems [2]. Also, it is useful for treating and detecting the risk of many diseases like diabetes mellitus, hypertension, cardiovascular disease, etc.…”

Section: Introductionmentioning

confidence: 99%

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

Sebastian,

Elharrouss,

Al-Maadeed

et al. 2023

Bioengineering

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Most diabetes patients develop a condition known as diabetic retinopathy after having diabetes for a prolonged period. Due to this ailment, damaged blood vessels may occur behind the retina, which can even progress to a stage of losing vision. Hence, doctors advise diabetes patients to screen their retinas regularly. Examining the fundus for this requires a long time and there are few ophthalmologists available to check the ever-increasing number of diabetes patients. To address this issue, several computer-aided automated systems are being developed with the help of many techniques like deep learning. Extracting the retinal vasculature is a significant step that aids in developing such systems. This paper presents a GAN-based model to perform retinal vasculature segmentation. The model achieves good results on the ARIA, DRIVE, and HRF datasets.

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A Survey on Deep-Learning-Based Diabetic Retinopathy Classification

Cited by 37 publications

References 115 publications

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Quantum Machine Learning for Ocular Disease Recognition

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

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