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January 1, 2026INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTSOpen Access

Exploring Deep Learning Architectures for Retinal Image Analysis in Diabetic Retinopathy

Swati Sumit Vaidya Latika Jindal

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Key Points

The aim is to evaluate various deep learning architectures for their ability to analyze retinal images in the diagnosis of diabetic retinopathy.
Review of multiple deep learning models
Comparative analysis of performance metrics
Image data set utilization for training and validation
Certain architectures significantly improved diagnostic accuracy
Deep learning models outperformed traditional methods
Findings suggest enhancements in retinal disease detection

Implication

This analysis explores the effectiveness of deep learning algorithms for diagnosing diabetic retinopathy, suggesting advancements in medical imaging.

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Cite This Study

Vaidya et al. (2026) studied this question.

synapsesocial.com/papers/69abc1d75af8044f7a4eae46 https://doi.org/10.56975/ijcrt.v14i2.301057

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