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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
Call For Paper - Volume 14 | Issue 4 | Month- April 2026
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Paper Title
DEEP LEARNING FOR DIABETIC RETINOPATHY DETECTION AND CLASSIFICATION BASED ON FUNDUS IMAGES
Authors
Smita Shrirang Satav, Dinesh B. Hanchate, 3Sachin S. Bere
Keywords
VGGNet ,ResNet, and DenseNet
Abstract
Diabetic retinopathy (DR) is a severe complication of diabetes that damages the retina and can lead to vision loss. Early detection and treatment of DR are crucial to prevent blindness. This study proposes using deep learning techniques to automatically detect and classify DR severity from fundus images. The methodology involves collecting fundus images, pre-processing them, training convolutional neural network models, and evaluating model performance using common classification metrics. Various deep learning architectures such as VGGNet, ResNet, and DenseNet will be explored. The models will be trained on large datasets such as Kaggle Diabetic Retinopathy Detection dataset. If successful, the deep learning model can be integrated into screening systems to detect DR early and enable timely treatment. Te process has the capability of reducing vision impairment that is noticed in DR globally.
IJCRT's Publication Details
Unique Identification Number - IJCRT24A5247
Paper ID - 261807
Page Number(s) - l60-l63
Pubished in - Volume 12 | Issue 5 | May 2024
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Smita Shrirang Satav, Dinesh B. Hanchate, 3Sachin S. Bere, "DEEP LEARNING FOR DIABETIC RETINOPATHY DETECTION AND CLASSIFICATION BASED ON FUNDUS IMAGES", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.l60-l63, May 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A5247.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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