Glaucoma Identification in Digital Fundus Images using Deep Learning Enhanced Auto Encoder Networks (DL-EAEN) for Accurate Diagnosis

R Sathya; P Balamurugan

doi:10.17485/IJST/v16i44.2325

Article

Glaucoma Identification in Digital Fundus Images using Deep Learning Enhanced Auto Encoder Networks (DL-EAEN) for Accurate Diagnosis

VIEWS 372
PDF 90

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i44.2325

Year: 2023, Volume: 16, Issue: 44, Pages: 4026-4037

Original Article

Glaucoma Identification in Digital Fundus Images using Deep Learning Enhanced Auto Encoder Networks (DL-EAEN) for Accurate Diagnosis

R Sathya^1*, P Balamurugan²

¹Research Scholar, PG & Research Department of Computer Science, Government Arts College, Coimbatore, Tamil Nadu, India
²Associate Professor, PG & Research Department of Computer Science, Government Arts College, Coimbatore, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:13 September 2023, Accepted Date:26 September 2023, Published Date:21 November 2023

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

Abstract

Objectives: To propose a novel method to enhance glaucoma identification by leveraging Digital Fundus Images (DFI). The deep learning-based approach, along with feature detection techniques, is utilized to discover the built-in features of the DFI in an unsupervised manner to enable robust detection with high accuracy. Methods: The Enhanced Auto Encoder Networks (DL-EAEN) approach is used to evaluate the latent representations from DFI and identify the morphological changes associated with glaucoma for prompt identification and classification. The fundus images are utilized for optic disc localization and glaucoma detection, and the Scale-Invariant Feature Transform (SIFT) approach is used to identify the local features as well as significant spots in the images. The PAPILA retinal dataset is used for this study with a record of 244 patients, which includes 488 fundus images of the left and right eye of all the patients in the M & F category with clinical results of healthy, glaucoma, suspect, and eye with crystalline and with IOL. In order to construct pixel-level masks and define the outer edges of the optic cup, U-Net and Mask-R-CNN techniques are employed as an image segmentation process. To measure the performance of DL-EAEN, MATLAB software is used and compared against existing models such as SVM, Adaboost, and CNN-Softmax classifiers. Findings: The proposed deep learning based enhanced AEN method outperforms the prevailing methods of SVM, Adaboost, and CNN-Softmax classifiers with promising results of 95.6% accuracy, 0.8 dice-score, 96.2% sensitivity, 97.01% specificity, 97.08% F-score, 97.41% precision, 98.02% recall, and AUC-ROC with 0.89 TPR & 0.16 FPR. Novelty: The evident results of DL-EAEN shows accurate and consistent rate in glaucoma detection and classification, which helps ophthalmologists, make easy diagnosis. In terms of accuracy, dice score, AUC-ROC, sensitivity, specificity, precision and F-score, the DL-EAEN overcomes the limitations of existing models SVM, Adaboost, and CNN-Softmax classifiers.

Keywords: Auto Encoder Networks, Deep Learning, Glaucoma Identification, Classification, Machine Learning, Data Mining, Image Segmentation

References

Chen HSL, Chen GA, Syu JY, Chuang LH, Su WW, Wu WC, et al. Early Glaucoma Detection by Using Style Transfer to Predict Retinal Nerve Fiber Layer Thickness Distribution on the Fundus Photograph. Ophthalmology Science. 2022;(3) 100180. Available from: https://doi.org/10.1016/j.xops.2022.100180
Mahum R, Rehman SU, Okon OD, Alabrah A, Meraj T, Rauf HT. A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging. Electronics. 2022;11(1):26. Available from: https://doi.org/10.3390/electronics11010026
Sudhan MB, Sinthuja M, Raja SP, Amutharaj J, Latha GCP, Rachel SS, et al. Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model. Journal of Healthcare Engineering. 2022;2022:1–10. Available from: https://doi.org/10.1155/2022/1601354
Shyamalee T, Meedeniya D. Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification. Machine Intelligence Research. 2022;19(6):563–580. Available from: https://doi.org/10.1007/s11633-022-1354-z
Nithyanandh S, Jaiganesh V. Quality of service enabled intelligent water drop algorithm based routing protocol for dynamic link failure detection in wireless sensor network. Indian Journal of Science and Technology. 2020;13(16):1641–1647. Available from: https://doi.org/ 10.17485/IJST/v13i16.19
Bowd C, Belghith A, Christopher M, Goldbaum MH, Fazio MA, Girkin CA, et al. Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest. Translational Vision Science & Technology. 2021;10(8):19. Available from: https://doi.org/10.1167/tvst.10.8.19
Linyu Z, Li T, Min X, Guofan C. The application of artificial intelligence in glaucoma diagnosis and prediction. Frontiers in Cell and Developmental Biology. 2023;11. Available from: https://doi.org/10.3389/fcell.2023.1173094
Gheisari S, Shariflou S, Phu J, Kennedy PJ, Agar A, Kalloniatis M, et al. A combined convolutional and recurrent neural network for enhanced glaucoma detection. Scientific Reports. 1945;11(1). Available from: https://doi.org/10.1038/s41598-021-81554-4
Nithyanandh S, Omprakash S, Megala D, Karthikeyan MP. Energy Aware Adaptive Sleep Scheduling and Secured Data Transmission Protocol to enhance QoS in IoT Networks using Improvised Firefly Bio-Inspired Algorithm (EAP-IFBA) Indian Journal Of Science And Technology. 2023;16(34):2753–2766.
Price DA, Harris A, Siesky B, Mathew S. The Influence of Translaminar Pressure Gradient and Intracranial Pressure in Glaucoma: A Review. Journal of Glaucoma. 2020;29(2):141–146. Available from: https://doi.org/10.1097/ijg.0000000000001421
Guergueb T, Akhloufi MA. A Review of Deep Learning Techniques for Glaucoma Detection. 2023. Available from: https://doi.org/10.1007/s42979-023-01734-z
Velpula VK, Sharma LD. Automatic Glaucoma Detection from Fundus Images Using Deep Convolutional Neural Networks and Exploring Networks Behaviour Using Visualization Techniques. SN Computer Science. 2023;4(5):487. Available from: https://doi.org/10.1007/s42979-023-01945-4
Nithyanandh S, Jaiganesh V. Reconnaissance Artificial Bee Colony Routing Protocol to Detect Dynamic Link Failure in Wireless Sensor Network. International Journal of Scientific & Technology Research. 2020;10(10):3244–3251. Available from: https://doi.org/10.35940/ijstr.b2271.0986231
Ajitha S, Akkara JD, Judy MV. Identification of glaucoma from fundus images using deep learning techniques. Indian Journal of Ophthalmology. 2021;(10) 2702–2709. Available from: https://doi.org/10.4103/ijo.IJO_92_21
Shinde R. Glaucoma detection in retinal fundus images using U-Net and supervised machine learning algorithms. Intelligence-Based Medicine. 2021;5:100038. Available from: https://doi.org/10.1016/j.ibmed.2021.100038
Kovalyk O, Morales-Sánchez J, Verdú-Monedero R, Sellés-Navarro I, Palazón-Cabanes A, Sancho-Gómez JL. PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment. Scientific Data. 2022;9(1). Available from: https://doi.org/10.1038/s41597-022-01388-1
Joshi S, Partibane B, Hatamleh WA, Tarazi HS, Yadav CS, Krah D. Glaucoma Detection Using Image Processing and Supervised Learning for Classification. Journal of Healthcare Engineering. 2022;2022:1–12. Available from: https://doi.org/10.1155/2022/2988262
Nawaz M, Nazir T, Javed A, Tariq U, Yong HS, Khan MA, et al. An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization. Sensors. 2022;22(2):434. Available from: https://doi.org/10.3390/s22020434
Divya J, Gaddipati J, Sivaswamy. Glaucoma Assessment from Fundus Images with Fundus to OCT Feature Space Mapping. ACM Transactions on Computing Healthcare. 2021;(1) 1–15. Available from: https://doi.org/10.1145/3470979
Raja PMS, Sumithra RP, Ramanan K. Glaucoma Detection with Fundus Images Based on NTKFIBC-IS Segmentation. Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. 2022;237:149–155. Available from: https://doi.org/10.1007/978-981-16-6407-6_14
Zedan MJM, Zulkifley MA, Ibrahim AA, Moubark AM, Kamari NAM, Abdani SR. Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review. Diagnostics. 2023;13(13):2180. Available from: https://doi.org/10.3390/diagnostics13132180
Akter N, Fletcher J, Perry S, Simunovic MP, Briggs N, Roy M. Glaucoma diagnosis using multi-feature analysis and a deep learning technique. Scientific Reports. 2022;12(1). Available from: https://doi.org/10.1038/s41598-022-12147-y
Shoukat, Ayesha, Akbar S, Hassan SA, Iqbal S, Mehmood A. Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach. Diagnostics. 2023. Available from: https://doi.org/10.3390/diagnostics13101738
Khan MS, Tafshir N, Alam KN, Dhruba AR, Khan MM, Albraikan AA, et al. Deep Learning for Ocular Disease Recognition: An Inner-Class Balance. Computational Intelligence and Neuroscience. 2022;p. 1–12. Available from: https://doi.org/10.1155/2022/5007111

Copyright

© 2023 Sathya & Balamurugan. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)