An Automated OCT-Based Glaucoma Detection with 3-D ResNet18 and a Convolutional Block Attention Module
Received: 16 September 2025 | Revised: 1 November 2025 | Accepted: 9 November 2025 | Online: 9 February 2026
Corresponding author: Venkateswara Rao Kalidindi
Abstract
In this work, a Deep Learning (DL) technique utilizing a 3D ResNet18 model supplemented with a Convolutional Block Attention Module (CBAM) was proposed to successfully extract volumetric characteristics while prioritizing the most informative spatial areas for classification. The data comprised labeled Optical Coherence Tomography (OCT) volumes processed with normalization, resizing, augmentation, and balanced splitting to minimize data leakage. The experimental results indicated that the model obtained stable performance, with validation and test AUC values of 0.9640 and 0.9495, respectively, and corresponding accuracy rates of 87.88% and 85.59%. Additionally, a comparison with previous models further validated the effectiveness of the current model. These results confirm that the framework can reliably achieve high sensitivity and specificity in separating glaucoma from normal cases.
Keywords:
OCT scans, glaucoma detection, 3D ResNet18, CBAMDownloads
References
A. N. Kuo, R. P. McNabb, and J. A. Izatt, "Advances in Whole-Eye Optical Coherence Tomography Imaging," Asia-Pacific Journal of Ophthalmology, vol. 8, no. 2, pp. 99–104, Mar. 2019.
K. C. Shih, R. H.-K. Tse, Y. T.-Y. Lau, and T. C.-Y. Chan, "Advances in Corneal Imaging: Current Applications and Beyond," The Asia-Pacific Journal of Ophthalmology, vol. 8, no. 2, Mar. 2019, Art. no. 105.
R. N. Weinreb and S. Moghimi, "Advances in Ocular Imaging," The Asia-Pacific Journal of Ophthalmology, vol. 8, no. 2, Mar. 2019, Art. no. 97.
J. Shan, C. DeBoer, and B. Y. Xu, "Anterior Segment Optical Coherence Tomography: Applications for Clinical Care and Scientific Research,”"Asia-Pacific Journal of Ophthalmology, vol. 8, no. 2, pp. 146–157, Mar. 2019.
R. Kapoor, B. T. Whigham, and L. A. Al-Aswad, "Artificial Intelligence and Optical Coherence Tomography Imaging," Asia-Pacific Journal of Ophthalmology, vol. 8, no. 2, pp. 187–194, Mar. 2019.
C. Y. Cheung, F. Tang, D. S. W. Ting, G. S. W. Tan, and T. Y. Wong, "Artificial Intelligence in Diabetic Eye Disease Screening," Asia-Pacific Journal of Ophthalmology, vol. 8, no. 2, pp. 158–164, Mar. 2019.
A. Ha and K. H. Park, "Optical Coherence Tomography for the Diagnosis and Monitoring of Glaucoma," Asia-Pacific Journal of Ophthalmology, vol. 8, no. 2, pp. 135–145, Mar. 2019.
R. K. Rasel, F. Wu, M. Chiariglione, S. S. Choi, N. Doble, and X. R. Gao, "Assessing the efficacy of 2D and 3D CNN algorithms in OCT-based glaucoma detection," Scientific Reports, vol. 14, no. 1, May 2024, Art. no. 11758. DOI: https://doi.org/10.1038/s41598-024-62411-6
X. Xie, G. Akiyama, T. Bogarin, S. Saraswathy, and A. S. Huang, "Visual Assessment of Aqueous Humor Outflow," Asia-Pacific Journal of Ophthalmology, vol. 8, no. 2, pp. 126–134, Mar. 2019.
A. Shabbir et al., "Detection of glaucoma using retinal fundus images: A comprehensive review," Mathematical Biosciences and Engineering, vol. 18, no. 3, pp. 2033–2076, Mar. 2021. DOI: https://doi.org/10.3934/mbe.2021106
M. Ashtari-Majlan, M. M. Dehshibi, and D. Masip, "Deep Learning and Computer Vision for Glaucoma Detection: A Review." arXiv, July 31, 2023.
P. Murala and K. N. Rao, "Multi-Class Brain Tumor Diagnosis Using a Vision Transformer with MRI Image Segmentation," Engineering, Technology & Applied Science Research, vol. 15, no. 4, pp. 26120–26127, Aug. 2025. DOI: https://doi.org/10.48084/etasr.11592
K. C. Zhou et al., "Computational 3D microscopy with optical coherence refraction tomography," Optica, vol. 9, no. 6, pp. 593–601, 2022. DOI: https://doi.org/10.1364/OPTICA.454860
I. A. El-Sadek et al., "Three-dimensional dynamics optical coherence tomography for tumor spheroid evaluation," Biomedical optics express, vol. 12, no. 11, pp. 6844–6863, 2021. DOI: https://doi.org/10.1364/BOE.440444
Y. George, B. J. Antony, H. Ishikawa, G. Wollstein, J. S. Schuman, and R. Garnavi, "Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images," IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 12, pp. 3421–3430, Dec. 2020. DOI: https://doi.org/10.1109/JBHI.2020.3001019
A. G. Podoleanu, J. A. Rogers, D. A. Jackson, and S. Dunne, "Three dimensional OCT images from retina and skin," Optics Express, vol. 7, no. 9, pp. 292–298, 2000. DOI: https://doi.org/10.1364/OE.7.000292
G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abramoff, and M. Sonka, "Three-Dimensional Analysis of Retinal Layer Texture: Identification of Fluid-Filled Regions in SD-OCT of the Macula," IEEE Transactions on Medical Imaging, vol. 29, no. 6, pp. 1321–1330, June 2010. DOI: https://doi.org/10.1109/TMI.2010.2047023
K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, "Segmentation of the Optic Disc in 3-D OCT Scans of the Optic Nerve Head," IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 159–168, Jan. 2010. DOI: https://doi.org/10.1109/TMI.2009.2031324
M. Niemeijer, M. K. Garvin, B. van Ginneken, M. Sonka, and M. D. Abràmoff, "Vessel segmentation in 3D spectral OCT scans of the retina," in Medical Imaging, San Diego, CA, USA, 2008. DOI: https://doi.org/10.1117/12.772680
H. Danesh, K. Maghooli, A. Dehghani, and R. Kafieh, "Synthetic OCT data in challenging conditions: three-dimensional OCT and presence of abnormalities," Medical & Biological Engineering & Computing, vol. 60, no. 1, pp. 189–203, 2022. DOI: https://doi.org/10.1007/s11517-021-02469-w
M. F. Kraus et al., "Quantitative 3D-OCT motion correction with tilt and illumination correction, robust similarity measure and regularization," Biomedical optics express, vol. 5, no. 8, pp. 2591–2613, 2014. DOI: https://doi.org/10.1364/BOE.5.002591
B. Antony et al., "Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images," Biomedical optics express, vol. 2, no. 8, pp. 2403–2416, 2011. DOI: https://doi.org/10.1364/BOE.2.002403
G. Altan, "DeepOCT: An explainable deep learning architecture to analyze macular edema on OCT images," Engineering Science and Technology, an International Journal, vol. 34, Oct. 2022, Art. no. 101091. DOI: https://doi.org/10.1016/j.jestch.2021.101091
L. Huo, J. Xi, Y. Wu, and X. Li, "Forward-viewing resonant fiber-optic scanning endoscope of appropriate scanning speed for 3D OCT imaging," Optics express, vol. 18, no. 14, pp. 14375–14384, 2010. DOI: https://doi.org/10.1364/OE.18.014375
B. Považay et al., "Impact of enhanced resolution, speed and penetration on three-dimensional retinal optical coherence tomography," Optics Express, vol. 17, no. 5, pp. 4134–4150, 2009. DOI: https://doi.org/10.1364/OE.17.004134
H. Liang et al., "En-face optical coherence tomography-a novel application of non-invasive imaging to art conservation," Optics Express, vol. 13, no. 16, pp. 6133–6144, 2005. DOI: https://doi.org/10.1364/OPEX.13.006133
M. Rahil, B. N. Anoop, G. N. Girish, A. R. Kothari, S. G. Koolagudi, and J. Rajan, "A Deep Ensemble Learning-Based CNN Architecture for Multiclass Retinal Fluid Segmentation in OCT Images," IEEE Access, vol. 11, pp. 17241–17251, Feb. 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3244922
S. B. Ploner et al., "Efficient and high accuracy 3-D OCT angiography motion correction in pathology," Biomedical Optics Express, vol. 12, no. 1, pp. 125–146, 2020. DOI: https://doi.org/10.1364/BOE.411117
Glaucoma Detection Dataset.
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Copyright (c) 2025 Venkateswara Rao Kalidindi, Harinadh Varikuti, Manikanta Kalyan Choppa, Priyanth Dunna, Venkat Rammohan Gummalla, Sireesha Malla
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