Enhanced Effective Baseline Model for Glaucoma Prediction Using U-Net and Hyperparameter-Tuned CNN with CLAHE

P.Revathi; Dr. R.Jayaprakash

doi:https://doi.org/10.47001/JAIET/2026.301006

Authors

P.Revathi

Research Scholar, Department of Computer Science, NGM College, Pollachi, Tamilnadu, India

Dr. R.Jayaprakash

Assistant Professor, Department of Computer Science, NGM College, Pollachi, Tamilnadu, India

Abstract

Glaucoma is a progressive eye disease that can lead to permanent vision loss when diagnosis is delayed. Our study proposes an enhanced effective baseline framework for automated glaucoma prediction from retinal fundus images by integrating image enhancement, segmentation, deep feature extraction, and machine learning classification respectively. The methods Contrast limited adaptive histogram equalization is applied to improve image contrast and reveal important retinal structures. The U-Net architecture is then employed to segment the optic disc region and reduce the influence of irrelevant retinal areas respectively. The segmented regions are subsequently processed using a hyper parameter-tuned Convolutional Neural Network to learn discriminative deep features efficiently. The optimized features are provided to an enhanced support vector machine classifier for glaucoma prediction. The hyper parameters of the CNN are systematically optimized to obtain more representative features and improve classification performance effectively. The incorporation of contrast enhancement, U-Net segmentation, optimized deep feature learning, and Enhanced Support Vector Machine provides an effective framework for automated glaucoma screening and early identification of glaucomatous changes respectively.

Keywords

Glaucoma prediction retinal fundus images Contrast Limited Adaptive Histogram Equalization U-Net Convolutional Neural Network hyperparameter tuning Enhanced Support Vector Machine optic disc segmentation

Citation of this Article

P.Revathi, & Dr. R.Jayaprakash. (2026). Enhanced Effective Baseline Model for Glaucoma Prediction Using U-Net and Hyperparameter-Tuned CNN with CLAHE. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 3(1), 35-39. Article DOI: https://doi.org/10.47001/JAIET/2026.301006

References

A.Diaz-Pinto, et al., “CNNs for automatic glaucoma assessment using fundus images: An extensive validation,” Bio Medical Engineering OnLine, vol. 18, Art. no. 29, 2019.
S. Ajitha, et al., “Identification of glaucoma from fundus images using deep learning techniques,” Indian Journal of Ophthalmology, vol. 69, no. 10, pp. 2702–2709, 2021.
S. Gheisari, et al., “A combined convolutional and recurrent neural network for enhanced glaucoma detection,” Scientific Reports, vol. 11, Art. no. 1945, 2021.
P. S. Mangipudi, et al., “Improved optic disc and cup segmentation in glaucomatic images using deep learning architecture,” Multimedia Tools and Applications, vol. 80, pp. 30143–30163, 2021.
L. Pascal et al., “Multi-task deep learning for glaucoma detection from color fundus images,” Scientific Reports, vol. 12, Art. no. 12361, 2022.
V. Sunanthini, J. et al., “Comparison of CNN algorithms for feature extraction on fundus images to detect glaucoma,” Journal of Healthcare Engineering, vol. 2022, Art. no. 7873300, 2022.
S. Elangovan et al., “A novel approach for glaucoma detection from color fundus images using ensemble of deep convolutional neural networks,” International Journal of Imaging Systems and Technology, vol. 32, no. 6, pp. 2034–2048, 2022.
S. Schiesser, J. et al., “Blood vessel segmentation using U-Net for glaucoma diagnosis with limited data,” Studies in Health Technology and Informatics, vol. 302, pp. 581–585, 2023.
X. Tian et al., “Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening,” Neural Computing and Applications, vol. 33, 2021.
L. Civit-Masot et al., “A comprehensive review of artificial intelligence models for screening major retinal diseases,” Artificial Intelligence Review, vol. 57, 2024.
H. M. Pandey, et al., “Improved optic disc and cup segmentation in glaucomatic images using deep learning architecture,” Multimedia Tools and Applications, vol. 80, pp. 30143–30163, 2021.

[1] A.Diaz-Pinto, et al., “CNNs for automatic glaucoma assessment using fundus images: An extensive validation,” Bio Medical Engineering OnLine, vol. 18, Art. no. 29, 2019.

[2] S. Ajitha, et al., “Identification of glaucoma from fundus images using deep learning techniques,” Indian Journal of Ophthalmology, vol. 69, no. 10, pp. 2702–2709, 2021.

[3] S. Gheisari, et al., “A combined convolutional and recurrent neural network for enhanced glaucoma detection,” Scientific Reports, vol. 11, Art. no. 1945, 2021.

[4] P. S. Mangipudi, et al., “Improved optic disc and cup segmentation in glaucomatic images using deep learning architecture,” Multimedia Tools and Applications, vol. 80, pp. 30143–30163, 2021.

[5] L. Pascal et al., “Multi-task deep learning for glaucoma detection from color fundus images,” Scientific Reports, vol. 12, Art. no. 12361, 2022.

[6] V. Sunanthini, J. et al., “Comparison of CNN algorithms for feature extraction on fundus images to detect glaucoma,” Journal of Healthcare Engineering, vol. 2022, Art. no. 7873300, 2022.

[7] S. Elangovan et al., “A novel approach for glaucoma detection from color fundus images using ensemble of deep convolutional neural networks,” International Journal of Imaging Systems and Technology, vol. 32, no. 6, pp. 2034–2048, 2022.

[8] S. Schiesser, J. et al., “Blood vessel segmentation using U-Net for glaucoma diagnosis with limited data,” Studies in Health Technology and Informatics, vol. 302, pp. 581–585, 2023.

[9] X. Tian et al., “Joint optic disc and cup segmentation based on multi-scale feature analysis and attention pyramid architecture for glaucoma screening,” Neural Computing and Applications, vol. 33, 2021.

[10] L. Civit-Masot et al., “A comprehensive review of artificial intelligence models for screening major retinal diseases,” Artificial Intelligence Review, vol. 57, 2024.

[11] H. M. Pandey, et al., “Improved optic disc and cup segmentation in glaucomatic images using deep learning architecture,” Multimedia Tools and Applications, vol. 80, pp. 30143–30163, 2021.