Retinal Disease Detection and Classification Using Convolution Neural Networks and Transfer Learning
- DOI
- 10.2991/978-94-6463-754-0_61How to use a DOI?
- Keywords
- OCT images; CNN; transfer learning; deep learning; feature extraction
- Abstract
The categorization of retinal diseases utilizing Optical Coherence Tomography (OCT) images has garnered considerable interest in the domain of computational medical imaging. This research paper introduces a deep learning methodology for the automated classification of OCT images via a custom Convolution Neural Network (CNN) in conjunction with recognized transfer learning models. Deep learning methodologies are favored for their exceptional accuracy and performance; however, they frequently necessitate substantial computational resources and time for training. MobileNet, ResNet50, GoogleNet, and DenseNet are employed for feature extraction and comparison analysis. The models are trained and refined on an OCT dataset utilizing various hyperparameter settings, such as learning rate, epoch count, and optimizing techniques to get maximal accuracy. Upon concluding our study, the performance of these models is evaluated, and the optimal design is ascertained based on accuracy and additional assessment criteria. Our methodology illustrates the capability of deep learning in the automated classification of OCT images, providing enhanced diagnostic support for the identification of retinal diseases.
- 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.
Cite this article
TY - CONF AU - Aditya Agarwal AU - Rohan Gupta AU - Abhinav Panwar AU - R. Loganathan AU - S. Latha AU - P. Muthu AU - Samiappan Dhanalakshmi PY - 2025 DA - 2025/06/30 TI - Retinal Disease Detection and Classification Using Convolution Neural Networks and Transfer Learning BT - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025) PB - Atlantis Press SP - 702 EP - 715 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6463-754-0_61 DO - 10.2991/978-94-6463-754-0_61 ID - Agarwal2025 ER -