Deep Diabetic: An Identification System of Diabetic Eye Diseases Using Deep Neural Network

Abstract: ❖The DeepDiabetic project revolutionizes the diagnosis of diabetic eye diseases, particularly diabetic retinopathy, by leveraging deep learning to automate and enhance the accuracy of retinal image analysis. Unlike traditional manual methods, which are slow and error-prone, DeepDiabetic employs the VGG-16 convolutional neural network to efficiently classify disease severity and detect related conditions. Advanced techniques, such as attention mechanisms and GAN-based data augmentation, improve feature extraction and address data limitations. Offering superior accuracy and a user-friendly interface, it streamlines clinical workflows and holds potential for broader application to other retinal disorders, advancing ophthalmic healthcare.

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-05-18
DOI
https://doi.org/10.55041/ijsrem48139
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Deep Diabetic: An Identification System of Diabetic Eye Diseases Using Deep Neural Network

M. S.
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Retinal Imaging and Analysis
article

Deep Diabetic: An Identification System of Diabetic Eye Diseases Using Deep Neural Network

M. S.
article en

Abstract

Abstract: ❖The DeepDiabetic project revolutionizes the diagnosis of diabetic eye diseases, particularly diabetic retinopathy, by leveraging deep learning to automate and enhance the accuracy of retinal image analysis. Unlike traditional manual methods, which are slow and error-prone, DeepDiabetic employs the VGG-16 convolutional neural network to efficiently classify disease severity and detect related conditions. Advanced techniques, such as attention mechanisms and GAN-based data augmentation, improve feature extraction and address data limitations. Offering superior accuracy and a user-friendly interface, it streamlines clinical workflows and holds potential for broader application to other retinal disorders, advancing ophthalmic healthcare.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(05)
Openalex Percentile: Top 84%
Retinal Imaging and Analysis
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