Detecting Diabetic Retinopathy Using Deep Learning Technique with Resnet-50

Jiwane, Viraj; DattaGupta, Anubhav; Chauhan, Arunkumar; Patil, Vidya

doi:10.1007/978-981-16-3690-5_5

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…show that the recent methods can obtain DR grading with an accuracy of between 75% and 85%. The current deep learning framework for detecting and grading DR is 9]. However, the disadvantages of ResNet-50 are over tting and uctuations in accuracy, which then affect the accuracy of detecting DR.…”

Section: Introductionmentioning

confidence: 99%

“…Revised structure of ResNet-50To solve the classi cation problems, many different types of ResNets are used, with different numbers of layers: speci cally, 18, 34, 50, 101, and 152 layers[16]. The current deep learning framework for detecting and grading DR is9]. However, the disadvantages of ResNet-50 are over tting and uctuations in accuracy, which affect its accuracy in detecting DR.…”

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confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Development of Revised ResNet-50 for Diabetic Retinopathy Detection

Lin

2023

Preprint

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A comprehensive review of artificial intelligence models for screening major retinal diseases

Hassan,

Raja,

Hassan

et al. 2024

Artif Intell Rev

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This paper provides a systematic survey of artificial intelligence (AI) models that have been proposed over the past decade to screen retinal diseases, which can cause severe visual impairments or even blindness. The paper covers both the clinical and technical perspectives of using AI models in hosipitals to aid ophthalmologists in promptly identifying retinal diseases in their early stages. Moreover, this paper also evaluates various methods for identifying structural abnormalities and diagnosing retinal diseases, and it identifies future research directions based on a critical analysis of the existing literature. This comprehensive study, which reviews both the conventional and state-of-the-art methods to screen retinopathy across different modalities, is unique in its scope. Additionally, this paper serves as a helpful guide for researchers who want to work in the field of retinal image analysis in the future.

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