Towards Accurate Glaucoma Identification: GAN-Enhanced Synthesis and Classification Using Pretrained MobileNetV2
By: Govindharaj I, G. Karthick and G. Michael

References
- Sarhan, A., Rokne, J., & Alhajj, R., “Glaucoma Detection Using Image Processing Techniques: A Literature Review, ” Computerized Medical Imaging and Graphics, vol. 78, pp. 101657, 2019, doi: 10.1016/j.compmedimag.2019.101657
- Anindita, S., & Agus, H., “Automatic Glaucoma Detection Based on the Type of Features Used: A Review, ” Journal of Theoretical and Applied Information Technology, vol. 72, no. 3, pp. 366–375, 2015.
- Abbas, Q., “Glaucoma-Deep: Detection of Glaucoma Eye Disease on Retinal Fundus Images Using Deep Learning, ” International Journal of Advanced Computer Science and Applications, vol. 8, no. 6, 2017, doi: 10.14569/IJACSA.2017.080606.
- Maheshwari, S., Pachori, R. B., & Acharya, U. R., “Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Corr-Entropy Features Extracted From Fundus Images, ” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 3, pp. 803–813, 2017.
- Dey, A., & Bandyopadhyay, S. K., “Automated Glaucoma Detection Using Support Vector Machine Classification Method, ” British Journal of Medicine and Medical Research, vol. 11, no. 12, 2016, doi: 10.9734/BJMMR/2016/19617.
- Claro, M., Santos, L., Silva, W., Araujo, F., Moura, N., & Macedo, A., “Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction, ” CLEI Electronic Journal, vol. 19, no. 2, pp. 1–10, 2016, doi: 10.19153/cleiej.19.2.4.
- Soman, A., & Mathew, D., “Glaucoma Detection and Segmentation Using Retinal Images, ” International Journal of Science, Engineering and Technology Research, vol. 5, no. 5, pp. 1346–1349, 2016.
- Odstrcilik, J., Kolar, R., Tornow, R., Jan, J., Budai, A., Mayer, M., & Vodakova, M., “Thickness Related Textural Properties of Retinal Nerve Fiber Layer in Color Fundus Images, ” Computerized Medical Imaging and Graphics, vol. 38, no. 6, pp. 508–516, 2014, doi: 10.1016/j.compmedimag.2014.05.005.
- Zilly, J., Buhmann, J. M., & Mahapatra, D., “Glaucoma Detection Using Entropy Sampling and Ensemble Learning for Automatic Optic Cup and Disc Segmentation, ” Computerized Medical Imaging and Graphics, vol. 55, pp. 28–41, 2017, doi: 10.1016/j.compmedimag.2016.07.012.
- Fu, H., Cheng, J., Xu, Y., Zhang, C., Wong, D. W. K., Liu, J., & Cao, X., “Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Images, ” IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2493–2501, 2018, doi: 10.1109/TMI.2018.2837012.
- Chen, X., Xu, Y., Wong, D. W. K., Wong, T. Y., & Liu, J., “Glaucoma Detection Based on Deep Convolutional Neural Network, ” in Proceedings of the IEEE Engineering in Medicine and Biology Conference, pp. 715–718, 2015, doi: 10.1109/EMBC.2015.7318462.
- Li, A., Cheng, J., Wong, D. W. K., & Liu, J., “Integrating Holistic and Local Deep Features for Glaucoma Classification, ” in Proceedings of the IEEE Engineering in Medicine and Biology Conference, pp. 1328–1331, 2016.
- Prastyo, P. H., Sumi, A. S., & Nuraini, A., “Optic Cup Segmentation Using U-Net Architecture on Retinal Fundus Images, ” Journal of Information Technology and Computer Engineering, vol. 4, no. 2, pp. 105–109, 2020, doi: 0.25077/jitce.4.02.105-109.2020.
- Li, L., Xu, M., Liu, H., et al., “A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection, ” IEEE Transactions on Medical Imaging, vol. 39, no. 2, pp. 413–424, 2020, doi: 10.1109/TMI.2019.2927226.
- El-Hag, N. A., Sedik, A., El-Shafai, W., et al., “Classification of Retinal Images Based on Convolutional Neural Networks, ” Microscopy Research and Technique, vol. 84, no. 3, pp. 394–414, 2021.
- Zhang, G., Pan, J., Zhang, Z., Xing, C., Sun, B., & Li, M., “Hybrid Graph Convolutional Network for Semi-Supervised Retinal Image Classification, ” IEEE Access, vol. 9, pp. 35778–35789, 2021.
- Sikder, N., Masud, M., Bairagi, A. K., et al., “Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm Through Analyzing Retinal Images, ” Symmetry, vol. 13, no. 4, pp. 670, 2021, doi: 10.3390/sym13040670.
- Bilal, A., Zhu, L., Deng, A., Lu, H., & Wu, N., “AI-Based Automatic Detection and Classification of Diabetic Retinopathy Using U-Net and Deep Learning, ” Symmetry, vol. 14, no. 7, pp. 1427, 2022, doi: 10.3390/sym14071427.
- Latif, J., Tu, S., Xiao, C., Rehman, S. U., Imran, A., & Latif, Y., “ODGNet: A Deep Learning Model for Automated Optic Disc Localization and Glaucoma Classification Using Fundus Images, ” SN Applied Sciences, vol. 4, pp. 98, 2022, doi: 10.1007/s42452-022-04984-3.
- Islam, M. T., Mashfu, S. T., Faisal, A., Siam, S. C., Naheen, I. T., & Khan, R., “Deep LearningBased Glaucoma Detection With Cropped Optic Cup and Disc and Blood Vessel Segmentation, ” IEEE Access, vol. 10, pp. 2828–2841, 2022.
- Thakur, N., Juneja, M., & Mittal, S., “Deep Learning-Based Glaucoma Detection: A Review, ” IEEE Access, vol. 9, pp. 68137–68156, 2021.
- Ran, A. R., Cheung, C. Y, et al., “Detection of Glaucoma Using Deep Learning From Retinal Fundus Photographs, ” Ophthalmology, vol. 129, pp. 113–121, 2022.
- Wang, H., et al., ‘Automated Glaucoma Screening Using Deep Learning Techniques, ” Computers in Biology and Medicine, vol. 143, pp. 105289, 2022.
- Zhao, X., et al., “Transformer-Based Glaucoma Detection in Retinal Fundus Images, ” IEEE Transactions on Medical Imaging, vol. 42, pp. 2541–2553, 2023.
- Ronneberger, O., Fischer, P., & Brox, T., “U-Net: Convolutional Networks for Biomedical Image Segmentation, ” in MICCAI, pp. 234–241, 2015.
- Çiçek, O., Abdulkadir, A., Lienkamp, S., Brox, T., & Ronneberger, O., “3D U-Net: Learning Dense Volumetric Segmentation From Sparse Annotation, ” in MICCAI, 2016.
- Oktay, O., Schlemper, J., et al., “Attention U-Net: Learning Where to Look for the Pancreas, ” in Medical Imaging with Deep Learning, 2018.
- Long, J., Shelhamer, E., & Darrell, T., “Fully Convolutional Networks for Semantic Segmentation, ” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
- Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J., “UNet++: A Nested U-Net Architecture for Medical Image Segmentation, ” Deep Learning in Medical Image Analysis, 2018.
- Chen, J., Lu, Y., Yu, Q., et al., “TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation, ” 2021.
- Doshi, R., et al., “Recent Advances in Deep Learning-Based Glaucoma Detection, ” Artificial Intelligence in Medicine, 2023.
- Zhang, Z., Yin, F. S., Liu, J., Wong, W. K., Tan, N. M., Lee, B. H., Cheng, J., & Wong, T Y (2010). “ORIGA-Light: An Online Retinal Fundus Image Database for Glaucoma Analysis and Research.” In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3065–3068).
Language: English
Page range: 95 - 105
Submitted on: Feb 19, 2024
Accepted on: May 15, 2024
Published on: Jun 24, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Related subjects:
© 2026 Govindharaj I, G. Karthick, G. Michael, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.