doi: 10.7763/IJCTE.2025.V17.1379
Multiclass Eye Disease Classification Using Transfer Learning Approach
Alvin Ming Siang Choo1
,
Stephanie Chua1,* ,
Lik Thai Lim2 ,
Dayang Nurfatimah Awang Iskandar1 ,
Mohd Hanafi Ahmad Hijazi3 ,
Puteri Nor Ellyza Nohuddin4
- 1Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
- 2Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
- 3Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
- 4Higher Colleges of Technology, Sharjah, United Arab Emirates
- Manuscript receivedFebruary 28, 2025
- revisedApril 22, 2025
- acceptedJune 13, 2025
- publishedOctober 20, 2025
Abstract
Ophthalmologists commonly use retinal fundus images for diagnosis. Recently, automation of this process using deep learning has gained significant attention. Multiclass classification, which distinguishes among multiple eye diseases, is more representative of actual clinical settings, however, it presents challenges such as limited availability of annotated datasets, class imbalance, overlapping clinical features across various eye diseases and disease heterogeneity. This study develops deep learning models for multiclass classification of three major eye diseases—cataracts, diabetic retinopathy, and glaucoma—alongside normal cases. A larger and more diverse dataset was obtained by combining multiple publicly available, well-annotated datasets. Four deep learning models: VGG16, Inception-v3, ResNet50 and EfficientNet-B0, were deployed using a transfer learning approach. These models achieved test accuracies ranging from 74.29% to 78.79%, with ResNet50 performing the best, achieving an accuracy of 78.79%, precision of 80.04%, recall of 78.79%, and an F1-score of 78.76%. The results demonstrate the effectiveness of transfer learning for multiclass classification of eye diseases. Notably, the models were trained and evaluated on a heterogeneous dataset that simulates real-world variability in image acquisition, highlighting their generalization capabilities and robustness to inconsistency. The study provides valuable insights about the performance of pre-trained deep learning models under realistic conditions, supporting their potential as assistive diagnostic tools in actual clinical scenarios.
Keywords
- multiclass classification
- eye diseases
- retinal fundus images
- transfer learning
- Convolutional Neural Networks (CNNs)
- pre-trained deep learning models
How to Cite
Alvin Ming Siang Choo, Stephanie Chua, Lik Thai Lim, Dayang Nurfatimah Awang Iskandar, Mohd Hanafi Ahmad Hijazi, and Puteri Nor Ellyza Nohuddin, "Multiclass Eye Disease Classification Using Transfer Learning Approach," International Journal of Computer Theory and Engineering, vol. 17, no. 4, pp. 170-178, 2025. https://doi.org/10.7763/IJCTE.2025.V17.1379
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).