Evaluating Deep Learning Techniques for Early Detection of Glaucoma

Abstract

The goal of this study is to apply sophisticated computational methods to identify glaucoma, a major cause of vision impairment. The system analyzes ocular pictures to identify patterns suggestive of glaucoma using convolutional neural networks (CNN) and deep learning architectures like ResNet, VGG, EfficientNet, MobileNet, and DenseNet. The method improves classification accuracy by using optimized neural network topologies and organized datasets. A variety of preprocessing methods guarantee that the input data is refined, which enhances model performance. The framework is intended to support clinical decision-making by aiding in early detection. This study aims to increase the accuracy and efficiency of diagnostics by showcasing the possibilities of automated analysis in ophthalmology.

Introduction

Glaucoma, a leading cause of irreversible blindness, affects the optic nerve and requires early detection to prevent severe vision loss. Traditional diagnosis relies heavily on manual assessment, which is time-consuming and subjective. This study proposes an automated, deep learning-based system to classify glaucoma using ocular images, enhancing accuracy, efficiency, and scalability.

Core Approach

The proposed system leverages Convolutional Neural Networks (CNNs) and advanced architectures such as:

• ResNet (residual learning for deep networks)

• VGG (deep with uniform filter sizes)

• EfficientNet (scalable depth, width, resolution)

• MobileNet (lightweight with depthwise separable convolutions)

• DenseNet (dense connectivity for feature reuse)

These models are trained on retinal fundus images to extract features, classify disease stages, and detect abnormalities.

System Architecture

1. Data Loading: Collect structured, high-quality retinal image datasets.

2. Preprocessing: Improve image clarity via normalization, denoising, and resizing.

3. Feature Extraction: CNN-based methods detect spatial patterns and structural features.

4. Training & Testing: Deep learning models are trained with labeled data using optimization methods (e.g., SGD, Adam).

5. Evaluation: Accuracy, precision, recall, and F1-score assess model performance.

Literature Review Highlights

• Aamir et al.: Proposed a multi-level CNN for detecting and classifying glaucoma stages with 99.39% accuracy.

• Cho et al.: Used an ensemble of 56 CNNs to improve staging accuracy to 88.1%.

• Xue et al.: Combined fundus images, intraocular pressure, and visual fields in a multi-feature deep learning model with 84.2% accuracy.

• Amersfoort et al.: Introduced a deterministic method for uncertainty quantification outperforming traditional ensembles.

• Boukhennoufa et al.: Though focused on stroke rehab, highlighted the potential of sensors and machine learning in medical diagnostics.

Proposed Model Strengths

• Automated: Reduces subjectivity and manual errors.

• Efficient: Uses scalable, lightweight architectures like MobileNet for real-world deployment.

• Accurate: Evaluated on multiple datasets with high classification performance.

• Reliable: Incorporates uncertainty quantification (UQ) to improve confidence in predictions.

Conclusion

In summary, this experiment shows how well deep learning methods work to analyze ocular images and find patterns linked to glaucoma. Through the use of optimal neural network designs and organized datasets, the system improves model performance and classification accuracy. More accurate identification is made possible by the incorporation of preprocessing procedures, which guarantee refined input data. The suggested approach demonstrates how automated analysis can support decision-making procedures, increasing the effectiveness and dependability of classification. The study highlights how important sophisticated computational techniques are for increasing accuracy and helping to create more potent diagnostic techniques.

References

[1] Al-Beshri, A., Alasbali, T., Shaf, A., Irfan, M., Ali, T., Ali, G., Mahnashi, M.H., and Aamir, M. (2020). A multi-level deep convolutional neural network with adaptive thresholds for the detection and categorization of glaucoma. https://doi.org/10.3390/diagnostics10080602 2. Boukhennoufa, I., Zhai, X., Utti, [2] V., Jackson, J., and McDonald-Maier, K.D., 2022. Diagnostics 10 (8), 602. A systematic examination of wearable sensors and machine learning in the evaluation of post-stroke rehabilitation. Sign Process Control in Biomedicine 71, 103197. https://doi.org/10.1016/j. bspc.2021.103197 [3] In 2021, Cho, H., Hwang, Y.H., Chung, J.K., Lee, K.B., Park, J.S., Kim, H.-G., and Jeong, J.H. A deep learning ensemble approach that uses convolutional neural networks and fundus photos to classify glaucoma stages. 1516–1524 in Curr. Eye Res. 46 (10). 10.1080/02713683.2021.1900268 can be found at https://doi.org. [4] Huang, X., Xu, X., Li, X., Zheng, Y., Zhu, Z., Jin, K., Ye, J., Gong, W., Si, K., Xue, Y., Zhu, J., 2022. A deep learning system with multiple features to improve the speed and accuracy of glaucoma severity detection. Inf. J. Biomed. 136, 104233. doi.org/10.1016/j.jbi.2022.104233 for further information. [5] Teh, Y.W., Gal, Y., Smith, L., and Van Amersfoort, J. (2020). One deep deterministic neural network for estimating uncertainty. Proceedings of the 37th International Conference on Machine Learning, vol. 119, PMLR, pp. 9690–9700, in: III, H.D., Singh, A. (Eds.). Accessed at: https://proceedings.mlr.press/v119/van-amersfoort20a.html. [6] J.-Q. Shi, X.-L. Du, J. Eyre, X. Chen, Y. Guan, Wearable accelerometers for automated stroke rehabilitation evaluation in free-living settings, arXiv preprint arXiv:2009.08798 (2020). [7] C. Fernandez-Grana, A. Kaku, A. Parnandi, A. Venkatesan, N. Pandit, H. Schambra, ArXiv preprint arXiv:2004.08297 (2020): Towards data-driven stroke rehabilitation with wearable sensors and deep learning. [8] The Lancet Neurology, 19 (4) (2020), 348–360; C.M. Stinear, C.E. Lang, S. Zeiler, and W.D. Byblow, Stroke Rehabilitation: Progress and Difficulties. [9] K. Rathakrishnan, S.-N. Min, and S.J. Park, Assessment of electrocardiogram features for the diagnostic classification of stroke survivors, Applied Sciences 11 (1) (2021) 192. [10] Y. Kim, K.-S. Lee, H.-S. Park, S.H. Chae, JMIR mHealth and uHealth 8 (7) (2020), e17216. Prospective comparison study. Development and clinical assessment of a web-based upper limb home rehabilitation system for chronic stroke survivors utilizing a smartwatch and machine learning model [11] IEE 7010: A new standard for evaluating the consequences of artificial intelligence for human well-being, D. Schiff, A. Ayesh, L. Musikanski, and J.C. Havens, in: IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, 2020, pp. 2746–2753. [12] Inceptiontime: Identifying alexnet for time series categorization, by H.I. Fawaz, B. Lucas, G. Forestier, C. Pelletier, D.F. Schmidt, J. Weber, G.I. Webb, L. Idoumghar, P.-A. Muller, and F. Petitjean, Data Mining and Knowledge Discovery 34 (6) (2020) 1936–1962. [13] Quan, J., Budden, D., Barth-Maron, G., Hessel, M., van Hasselt, H., Silver, D., and Horgan, D. 8. replay of a distributed, prioritized experience. Learning Representations International Conference, 2018. [14] Haarnoja, T., Levine, S., Zhou, A., and Abbeel, P. Soft actor-critic: Deep reinforcement learning using a stochastic actor using off-policy maximum entropy. the 2018 arXiv preprint arXiv:1801.01290. [15] Schuurmans, D., Tucker, G., Norouzi, M., and Nachum, O. Gaussian policy learning using smoothed action value functions. 2018; arXiv preprint arXiv:180.30348.

Copyright

Copyright © 2025 Kirushnakand N, Karthic V, KarthickRaja K, Vijayalakshmi S. 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.