Customized Convolutional Neural Network for Glaucoma Detection in Retinal Fundus Images

Authors

Fajrul Islami , Sumijan , Sarjon Defit

DOI:

10.29303/jppipa.v10i8.7614

Published:

2024-08-25

Issue:

Vol. 10 No. 8 (2024): August

Keywords:

Customized Convolutional Neural Network, Deep Learning, Early Detection, Glaucoma, Retinal Fundus Image

Research Articles

Downloads

How to Cite

Islami, F., Sumijan, & Defit, S. (2024). Customized Convolutional Neural Network for Glaucoma Detection in Retinal Fundus Images. Jurnal Penelitian Pendidikan IPA, 10(8), 4606–4613. https://doi.org/10.29303/jppipa.v10i8.7614

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Abstract

Glaucoma is one of the leading causes of permanent blindness and remains a current challenge in the field of ophthalmology. This research aims to present a comprehensive investigation into the development and evaluation of new technology for glaucoma detection in retinal fundus images. The development and evaluation are presented on a customized architecture, using the Convolutional Neural Network (CNN) method. The proposed CNN architecture is designed to address the complex characteristics of glaucoma changes in the identification process. The research dataset consists of 506 retinal images categorized into 117 glaucoma images, 19 suspected glaucoma images, and 370 healthy images. Through our in-depth exploration, we conducted a careful analysis to uncover patterns and fundamental trends related to glaucoma-related features. During the training phase, the proposed CNN achieved outstanding average accuracy, sensitivity, and specificity values of 92.88%, 94.66%, and 89.31%, respectively. In the unseen test dataset, the model demonstrated competitive performance with an accuracy of 80.87%, sensitivity of 85.65%, and specificity of 71.26%. These findings emphasize the potential of the model as a reliable tool for glaucoma detection. The results indicate that the proposed method utilizing a customized CNN architecture is designed for glaucoma detection in retinal fundus images. The presented output results also hold promise for clinical relevance and can be considered an improvement in the care of retinal fundus patients.

References

Alayón, S., Hernández, J., Fumero, F. J., Sigut, J. F., & Díaz-Alemán, T. (2023). Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images. Applied Sciences, 13(23), 12722. https://doi.org/10.3390/app132312722

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 53. https://doi.org/10.1186/s40537-021-00444-8

Amin, J., Sharif, M., Anjum, M. A., Raza, M., & Bukhari, S. A. C. (2020). Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI. Cognitive Systems Research, 59, 304–311. https://doi.org/10.1016/j.cogsys.2019.10.002

Atalay, E., Özalp, O., Devecioğlu, Ö. C., Erdoğan, H., İnce, T., & Yıldırım, N. (2022). Investigation of the Role of Convolutional Neural Network Architectures in the Diagnosis of Glaucoma using Color Fundus Photography. Turkish Journal of Ophthalmology, 52(3), 193–200. https://doi.org/10.4274/tjo.galenos.2021.29726

Barros, D. M. S., Moura, J. C. C., Freire, C. R., Taleb, A. C., Valentim, R. A. M., & Morais, P. S. G. (2020). Machine learning applied to retinal image processing for glaucoma detection: Review and perspective. BioMedical Engineering OnLine, 19(1), 20. https://doi.org/10.1186/s12938-020-00767-2

Chen, F., & Tsou, J. Y. (2022). Assessing the effects of convolutional neural network architectural factors on model performance for remote sensing image classification: An in-depth investigation. International Journal of Applied Earth Observation and Geoinformation, 112, 102865. https://doi.org/10.1016/j.jag.2022.102865

Chiang, Y.-Y., Chen, C.-L., & Chen, Y.-H. (2024). Deep Learning Evaluation of Glaucoma Detection Using Fundus Photographs in Highly Myopic Populations. Biomedicines, 12(7), 1394. https://doi.org/10.3390/biomedicines12071394

Das, S., Kharbanda, K., M, S., Raman, R., & D, E. D. (2021). Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomedical Signal Processing and Control, 68, 102600. https://doi.org/10.1016/j.bspc.2021.102600

Elangovan, P., Nath, M. K., & Mishra, M. (2020). Statistical Parameters for Glaucoma Detection from Color Fundus Images. Procedia Computer Science, 171, 2675–2683. https://doi.org/10.1016/j.procs.2020.04.290

Ganokratanaa, T., Ketcham, M., & Pramkeaw, P. (2023). Advancements in Cataract Detection: The Systematic Development of LeNet-Convolutional Neural Network Models. Journal of Imaging, 9(10), 197. https://doi.org/10.3390/jimaging9100197

Geetha, A., & B. Prakash, N. (2022). Classification of Glaucoma in Retinal Images Using EfficientnetB4 Deep Learning Model. Computer Systems Science and Engineering, 43(3), 1041–1055. https://doi.org/10.32604/csse.2022.023680

Ghosh, A., Jana, N. D., Mallik, S., & Zhao, Z. (2022). Designing optimal convolutional neural network architecture using differential evolution algorithm. Patterns, 3(9), 100567. https://doi.org/10.1016/j.patter.2022.100567

Gómez-Valverde, J. J., Antón, A., Fatti, G., Liefers, B., Herranz, A., Santos, A., Sánchez, C. I., & Ledesma-Carbayo, M. J. (2019). Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomedical Optics Express, 10(2), 892. https://doi.org/10.1364/BOE.10.000892

Haider, A., Arsalan, M., Lee, M. B., Owais, M., Mahmood, T., Sultan, H., & Park, K. R. (2022). Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images. Expert Systems with Applications, 207, 117968. https://doi.org/10.1016/j.eswa.2022.117968

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90

Huang, Y.-C., Hung, K.-C., Liu, C.-C., Chuang, T.-H., & Chiou, S.-J. (2022). Customized Convolutional Neural Networks Technology for Machined Product Inspection. Applied Sciences, 12(6), 3014. https://doi.org/10.3390/app12063014

Joshi, S., Partibane, B., Hatamleh, W. A., Tarazi, H., Yadav, C. S., & Krah, D. (2022). Glaucoma Detection Using Image Processing and Supervised Learning for Classification. Journal of Healthcare Engineering, 2022, 1–12. https://doi.org/10.1155/2022/2988262

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., Dong, J., Prasadha, M. K., Pei, J., Ting, M. Y. L., Zhu, J., Li, C., Hewett, S., Dong, J., Ziyar, I., … Zhang, K. (2018). Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell, 172(5), 1122-1131. https://doi.org/10.1016/j.cell.2018.02.010

Kishore, B., & Ananthamoorthy, N. P. (2020). Glaucoma classification based on intra-class and extra-class discriminative correlation and consensus ensemble classifier. Genomics, 112(5), 3089–3096. https://doi.org/10.1016/j.ygeno.2020.05.017

Közkurt, C., Diker, A., Elen, A., Kılıçarslan, S., Dönmez, E., & Demir, F. B. (2024). Trish: An efficient activation function for CNN models and analysis of its effectiveness with optimizers in diagnosing glaucoma. The Journal of Supercomputing, 80(11), 15485–15516. https://doi.org/10.1007/s11227-024-06057-1

Krichen, M. (2023). Convolutional Neural Networks: A Survey. Computers, 12(8), 151. https://doi.org/10.3390/computers12080151

Li, F., Wang, Z., Qu, G., Song, D., Yuan, Y., Xu, Y., Gao, K., Luo, G., Xiao, Z., Lam, D. S. C., Zhong, H., Qiao, Y., & Zhang, X. (2018). Automatic differentiation of Glaucoma visual field from non-glaucoma visual filed using deep convolutional neural network. BMC Medical Imaging, 18(1), 35. https://doi.org/10.1186/s12880-018-0273-5

Lommatzsch, C., & Van Oterendorp, C. (2024). Current Status and Future Perspectives of Optic Nerve Imaging in Glaucoma. Journal of Clinical Medicine, 13(7), 1966. https://doi.org/10.3390/jcm13071966

N K, J., Ali, Md. H., Senthil, S., & Srinivas, M. B. (2024). Early detection of glaucoma: Feature visualization with a deep convolutional network. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 12(1), 2350508. https://doi.org/10.1080/21681163.2024.2350508

O’Reilly, M., Duffin, J., Ward, T., & Caulfield, B. (2017). Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation. JMIR Rehabilitation and Assistive Technologies, 4(2). https://doi.org/10.2196/rehab.7259

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211–252. https://doi.org/10.1007/s11263-015-0816-y

Saha, S., Vignarajan, J., & Frost, S. (2023). A fast and fully automated system for glaucoma detection using color fundus photographs. Scientific Reports, 13(1), 18408. https://doi.org/10.1038/s41598-023-44473-0

Shiga, Y., Nishida, T., Jeoung, J. W., Di Polo, A., & Fortune, B. (2023). Optical coherence tomography and optical coherence tomography angiography: Essential tools for detecting glaucoma and disease progression. Frontiers in Ophthalmology, 3, 1217125. https://doi.org/10.3389/fopht.2023.1217125

Shinde, R. (2021). Glaucoma detection in retinal fundus images using U-Net and supervised machine learning algorithms. Intelligence-Based Medicine, 5, 100038. https://doi.org/10.1016/j.ibmed.2021.100038

Shoukat, A., Akbar, S., Hassan, S. A., Iqbal, S., Mehmood, A., & Ilyas, Q. M. (2023). Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach. Diagnostics, 13(10), 1738. https://doi.org/10.3390/diagnostics13101738

Singh, L. K., Pooja, Garg, H., & Khanna, M. (2022). Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images. Multimedia Tools and Applications, 81(19), 27737–27781. https://doi.org/10.1007/s11042-022-12826-y

Sudhan, M. B., Sinthuja, M., Pravinth Raja, S., Amutharaj, J., Charlyn Pushpa Latha, G., Sheeba Rachel, S., Anitha, T., Rajendran, T., & Waji, Y. A. (2022). Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model. Journal of Healthcare Engineering, 2022, 1–10. https://doi.org/10.1155/2022/1601354

Taye, M. M. (2023). Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions. Computation, 11(3), 52. https://doi.org/10.3390/computation11030052

Tian, Z., Zheng, Y., Li, X., Du, S., & Xu, X. (2020). Graph convolutional network based optic disc and cup segmentation on fundus images. Biomedical Optics Express, 11(6), 3043. https://doi.org/10.1364/BOE.390056

Tsai, Y.-C., Lee, H.-P., Tsung, T.-H., Chen, Y.-H., & Lu, D.-W. (2024). Unveiling Novel Structural Biomarkers for the Diagnosis of Glaucoma. Biomedicines, 12(6), 1211. https://doi.org/10.3390/biomedicines12061211

Velpula, V. K., & Sharma, L. D. (2023). Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier fusion. Frontiers in Physiology, 14, 1175881. https://doi.org/10.3389/fphys.2023.1175881

Vijayan, M., Prasad, D. K., & S, V. (2024). Advancing Glaucoma Diagnosis: Employing Confidence-Calibrated Label Smoothing Loss for Model Calibration. Ophthalmology Science, 100555. https://doi.org/10.1016/j.xops.2024.100555

Wang, S., Kim, B., Kang, J., & Eom, D.-S. (2024). Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning. Applied Sciences, 14(11), 4588. https://doi.org/10.3390/app14114588

Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: An overview and application in radiology. Insights into Imaging, 9(4), 611–629. https://doi.org/10.1007/s13244-018-0639-9

Zhou, Q., Guo, J., Chen, Z., Chen, W., Deng, C., Yu, T., Li, F., Yan, X., Hu, T., Wang, L., Rong, Y., Ding, M., Wang, J., & Zhang, X. (2022). Deep learning-based classification of the anterior chamber angle in glaucoma gonioscopy. Biomedical Optics Express, 13(9), 4668. https://doi.org/10.1364/BOE.465286

Author Biographies

Fajrul Islami, University Putra Indonesia YPTK

Sumijan, University Putra Indonesia YPTK

Sarjon Defit, University Putra Indonesia YPTK

License

Copyright (c) 2024 Fajrul Islami, Sumijan, Sarjon Defit

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).