A Novel Approach for the Early Recognition of Diabetic Retinopathy using Machine Learning

Soni, Akanksha; Rai, Avinash

doi:10.1109/iccci50826.2021.9402566

Cited by 17 publications

(6 citation statements)

References 6 publications

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“…It was decided to make an automatic DR scoring system that could recognize pictures based on the pathology of the disease at four different levels of severity. A CNN merges an input picture with a weight matrix to pull out certain image traits while keeping information about how the image is arranged in space ( Soni & Rai, 2021 ).…”

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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“…Thus, retinal examinations remain a primary component of DR management, and they are essential for reducing the long-term consequences of the disease. It is di cult but of the utmost importance to recognize and treat DR, to avoid the more serious risk of permanent blindness in at-risk individuals [3].…”

Section: Introductionmentioning

confidence: 99%

Development of Revised ResNet-50 for Diabetic Retinopathy Detection

Lin

2023

Preprint

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Diabetic retinopathy (DR) produces bleeding, exudation, and new blood vessel formation conditions. DR can damage the retinal blood vessels and cause vision loss or even blindness. If DR is detected early, ophthalmologists can use lasers to create tiny burns around the retinal tears to inhibit bleeding and prevent the formation of new blood vessels, in order to prevent deterioration of the disease. Imaging examination is a valuable technique for doctors in the prediction and treatment of diseases. The rapid improvement of deep learning has made image recognition an effective technology; it can avoid misjudgments caused by different doctors’ evaluations and help doctors to predict the condition quickly. Therefore, this paper adopts visualization and preprocessing in the ResNet-50 model to improve module calibration, to enable the model to predict DR accurately.

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“…If DR is not diagnosed in the early stages, it causes impaired vision and may even lead to blindness. It is difficult but of the utmost importance to recognize and treat DR, to avoid the serious risk of permanent blindness in at‐risk individuals 3 …”

Section: Introductionmentioning

confidence: 99%

“…It is difficult but of the utmost importance to recognize and treat DR, to avoid the serious risk of permanent blindness in at-risk individuals. 3 The evolution of deep learning (DL) has made artificial intelligence (AI) image recognition a mature technology. 4 Many recent studies have applied DL and image recognition technology to the recognition of DR. [5][6][7][8][9][10][11] The results of the APTOS 2019 Blindness Detection study (https://www.kaggle.com/c/aptos2019-blindness-detection) revealed that the recent methods obtained an accuracy of 75%-85% for grading the DR level.…”

Section: Introductionmentioning

confidence: 99%

Development of preprocessing methods and revised EfficientNet for diabetic retinopathy detection

Lin

Jiang

2023

Int J Imaging Syst Tech

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A Novel Approach for the Early Recognition of Diabetic Retinopathy using Machine Learning

Cited by 17 publications

References 6 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

Development of Revised ResNet-50 for Diabetic Retinopathy Detection

Development of preprocessing methods and revised EfficientNet for diabetic retinopathy detection

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