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  Published Paper Details:

  Paper Title

DIABETIC RETINOPATHY CLASSIFICATION USING MULTI-SCALE AND TEMPORAL ATTENTION

  Authors

  Rahul Yadav

  Keywords

Diabetic retinopathy, deep learning, multi-scale CNN, temporal attention, image classification, explainable AI.

  Abstract


Diabetic retinopathy (DR) is a leading cause of blindness among diabetic patients. Timely screening and accurate severity grading are critical for effective intervention. This paper proposes a novel deep learning architecture that integrates a multi-scale convolutional neural network (CNN) with temporal attention to classify DR severity using color fundus images. Multi-scale feature extraction via a feature pyramid network captures lesions at different scales, while a bidirectional long short-term memory (LSTM) with attention aggregates temporal features from sequential visits. Experimental evaluation on public datasets, including Kaggle DR and EyePACS, demonstrates that our model outperforms baseline CNNs and state-of-the-art methods, achieving 92.4% accuracy and a Cohen's Kappa of 0.89. Grad-CAM visualizations highlight clinically significant lesions, ensuring model interpretability.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2506973

  Paper ID - 289941

  Page Number(s) - i267-i269

  Pubished in - Volume 13 | Issue 6 | June 2025

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Rahul Yadav,   "DIABETIC RETINOPATHY CLASSIFICATION USING MULTI-SCALE AND TEMPORAL ATTENTION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 6, pp.i267-i269, June 2025, Available at :http://www.ijcrt.org/papers/IJCRT2506973.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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