Convolutional Neural Network-Based Image Analysis for Early Diagnosis of Diabetic Retinopathy

DOI

10.2991/978-94-6463-704-5_5How to use a DOI?

Keywords

Convolutional Neural Network; Diabetic Retinopathy; Deep Learning; Detection

Abstract

Diabetic Retinopathy (DR) is a leading cause of vision impairment and blindness among diabetic patients worldwide. Early detection and timely intervention are crucial to preventing severe visual loss. Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image analysis and have shown great potential in medical image diagnostics. This study explores the application of CNNs for the early diagnosis of DR using retinal fundus images. By leveraging deep learning techniques, our CNN model is trained to automatically detect and classify various stages of DR with high accuracy. The proposed method utilizes a large dataset of annotated retinal images to ensure robustness and generalizability. Experimental results demonstrate that the CNN-based approach significantly outperforms traditional methods, offering a reliable and efficient solution for early DR detection. This advancement in automated image analysis can facilitate timely and precise diagnosis, potentially reducing the burden of diabetic complications and improving patient outcomes.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Vivek Kumar
AU  - Anmol Singh Gill
AU  - Ajay Kumar
PY  - 2025
DA  - 2025/04/30
TI  - Convolutional Neural Network-Based Image Analysis for Early Diagnosis of Diabetic Retinopathy
BT  - Proceedings of the International Conference on Smart Health and Intelligent Technologies (ICSHit-2024)
PB  - Atlantis Press
SP  - 33
EP  - 45
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-704-5_5
DO  - 10.2991/978-94-6463-704-5_5
ID  - Kumar2025
ER  -