Deep Learning - Powered Fundus Image Analysis For Ocular Disease Detection

DOI

10.2991/978-94-6463-858-5_63How to use a DOI?

Keywords

Ocular disease detection; EfficientNetB0; deep learning; retinal fundus images; medical imaging; classification; Transfer Learning; Automated diagnosis

Abstract

Timely diagnosis of ocular diseases is essential for preventing vision loss and ensuring effective treatment. This research proposes an autonomous deep learning-based system for detecting common eye diseases from retinal fundus images. The study utilizes the publicly available Ocular Disease Recognition dataset from Kaggle, which contains diverse, annotated retinal images. Our methodology involves pre-processing techniques for noise reduction and feature enhancement, followed by disease classification using EfficientNetB0, a highly efficient convolutional neural network (CNN) architecture optimized for performance and computational efficiency. The model is trained to identify four prevalent ocular diseases: glaucoma, cataract, age-related macular degeneration, and pathological myopia. The performance of the proposed system is evaluated using key metrics such as accuracy and precision, demonstrating its effectiveness in automated disease detection. By integrating AI-driven diagnosis into ophthalmology, this system offers a fast, noninvasive, and cost-effective solution, reducing the burden on healthcare professionals while enhancing early disease detection and 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  - B. Anish Kumar
AU  - C. Asheeq Akthar
AU  - T. C. Ahann
AU  - N. K. Abhishek
AU  - Shanid Malayil
AU  - A. K. Mubeena
PY  - 2025
DA  - 2025/11/04
TI  - Deep Learning - Powered Fundus Image Analysis For Ocular Disease Detection
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 743
EP  - 752
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_63
DO  - 10.2991/978-94-6463-858-5_63
ID  - Kumar2025
ER  -