Shinde R (2021) Glaucoma detection in retinal fundus images using U-Net and supervised machine learning algorithms. Intell Based Med 5:100038
Shinoj VK, Hong XJ, Murukeshan VM, Baskaran M, Tin A (2016) Progress in anterior chamber angle imaging for glaucoma risk prediction–a review on clinical equipment, practice and research. Med Eng Phys 38(12):1383–1391
Juneja M, Shaswat S, Naman A, Shivank B, Shubham G, Niharika T, Prashant J (2020) Automated detection of glaucoma using deep learning convolution network (G-net). Multimedia Tools Appl 79(21):15531–15553
Velpula VK, Vadlamudi J, Kasaraneni PP, Kumar YV (2024) Automated glaucoma detection in fundus images using comprehensive feature extraction and advanced classification techniques. Eng Proc 82(1):33
Haider A, Arsalan M, Lee MB, Owais M, Mahmood T, Sultan H, Park KR (2022) Artificial intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images. Expert Syst Appl 207:117968
Shyamalee T, Meedeniya D, Lim G, Karunarathne M (2024) Automated tool support for glaucoma identification with explainability using fundus images. IEEE Access 12:17290–17307
Jiang Y, Duan L, Cheng J, Gu Z, Xia H, Fu H, Li C, Liu J (2019) Jointrcnn: a region-based convolutional neural network for optic disc and cup segmentation. IEEE Trans Biomed Eng 67(2):335–343
Singh LK, Garg H, Khanna M, Bhadoria RS (2021) An analytical study on machine learning techniques. InMultidisciplinary functions of Blockchain technology in AI and IoT applications. 137–157. IGI Global Scientific Publishing.
Singh LK, Khanna M (2023) Introduction to artificial intelligence and current trends. InInnovations in Artificial Intelligence and Human-Computer Interaction in the Digital Era. 31–66. Academic Press.
Shyamalee T, Meedeniya D (2022) Glaucoma detection with retinal fundus images using segmentation and classification. Mach Intell Res 19(6):563–580
Santhosh S, Babu DV (2023) Retinal Glaucoma Detection from Digital Fundus Images using Deep Learning Approach. In2023 7th International Conference on Computing Methodologies and Communication (ICCMC). 68–72. IEEE.
Thanki R (2023) A deep neural network and machine learning approach for retinal fundus image classification. Healthc Anal 3:100140
Alghamdi M, Abdel-Mottaleb M (2021) A comparative study of deep learning models for diagnosing glaucoma from fundus images. IEEE Access 9:23894–23906
Fan R, Alipour K, Bowd C, Christopher M, Brye N, Proudfoot JA, Goldbaum MH, Belghith A, Girkin CA, Fazio MA, Liebmann JM (2023) Detecting glaucoma from fundus photographs using deep learning without convolutions: transformer for improved generalization. Ophthalmol Sci 3(1):100233
Sudhan MB, Sinthuja M, Pravinth Raja S, Amutharaj J, Charlyn Pushpa Latha G, Sheeba Rachel S, Anitha T, Rajendran T, Waji YA (2022) Segmentation and classification of glaucoma using u‐net with deep learning model. J Healthcare Eng 2022(1):1601354
Sangeethaa SN, Uma Maheswari P (2018) An intelligent model for blood vessel segmentation in diagnosing DR using CNN. J Med Syst 42(10):175
Article CAS PubMed Google Scholar
Sangeethaa SN (2023) Presumptive discerning of the severity level of glaucoma through clinical fundus images using hybrid polynet. Biomed Signal Process Control 81:104347
Sangeethaa SN, Jothimani S (2023) Detection of exudates from clinical fundus images using machine learning algorithms in diabetic maculopathy. Int J Diabetes Dev Ctries 43(1):25–35
Kotteeswari C, Sangeethaa SN, Jothimani S (2025) Advanced fusion network for detecting and grading macular edema in diabetic patients. International Journal of Diabetes in Developing Countries 1–26.
Nawar, Youssof, Nouran Soliman, Moustafa Wassel, Mohamed ElHabebe, Noha Adly, Marwan Torki, Ahmed Elmassry, and Islam Ahmed. "DiffuPT: Class Imbalance Mitigation for Glaucoma Detection via Diffusion Based Generation and Model Pretraining." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 4098–4107. IEEE, 2025.
Parashar D, Agrawal DK (2020) Automated classification of glaucoma stages using flexible analytic wavelet transform from retinal fundus images. IEEE Sens J 20(21):12885–12894
Virbukaitė S, Bernatavičienė J, Imbrasienė D (2024) Glaucoma identification using convolutional neural networks ensemble for optic disc and cup segmentation. IEEE Access 12:82720–82729
Dorathy DK, Raj L, Kautish S, Almazyad A, Sallam K, Ali M (2024) Fuzzy difference equations in diagnoses of glaucoma from retinal images using deep learning. Comput Model Eng Sci 139(1):801
Shoukat A, Akbar S, Hassan SA, Iqbal S, Mehmood A, Ilyas QM (2023) Automatic diagnosis of glaucoma from retinal images using deep learning approach. Diagnostics (Basel) 13(10):1738
Govindan M, Dhakshnamurthy VK, Sreerangan K, Nagarajan MD, Rajamanickam SK (2024) A framework for early detection of glaucoma in retinal fundus images using deep learning. Engineering Proceedings 62(1):3
Aljohani A, Aburasain RY (2024) A hybrid framework for glaucoma detection through federated machine learning and deep learning models. BMC Med Inform Decis Mak 24(1):115
Article PubMed PubMed Central Google Scholar
Song WT, Lai C, Su YZ (2021) A statistical robust glaucoma detection framework combining retinex, CNN, and DOE using fundus images. IEEE Access 9:103772–103783
Ghorui A, Chatterjee S, Makkar R, Pachiyappan A, Balamurugan S (2023) Deployment of CNN on colour fundus images for the automatic detection of glaucoma. Int J Appl Sci Eng 20(1):1–9
Bali A, Mansotra V (2024) Glaucoma diagnosis using hybrid neural encoder decoder based unet hybrid inception. Mansoura Eng J 49(4):11
Singh LK, Garg H, Khanna M (2021) An artificial intelligence-based smart system for early glaucoma recognition using OCT images. Int J E-Health Med Commun 12(4):32–59
Singh LK, Khanna M, Garg H, Singh R, Iqbal M (2024) A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images. Multimedia Tools Appl 83(37):85421–85481
Singh LK, Garg H, Khanna M (2022) Histogram of oriented gradients (HOG)-based artificial neural network (ANN) classifier for glaucoma detection. International Journal of Swarm Intelligence Research (IJSIR). Jan 1 13(1):1–32.
Singh LK, Garg H, Pooja KM (2020) Performance analysis of machine learning techniques for glaucoma detection based on textural and intensity features. Int J Innovative Comput Appl 11(4):216–230
Govindharaj I, Deva Priya W, Soujanya KL, Senthilkumar KP, Shantha Shalini K, Ravichandran S (2025) Advanced glaucoma disease segmentation and classification with grey wolf optimized U− net++ and capsule networks. Int Ophthalmol 45(1):1–24
Govindharaj I, Rampriya R, Michael G, Yazhinian S, Dinesh Kumar K, Anandh R (2025) Capsule network-based deep learning for early and accurate diabetic retinopathy detection. Int Ophthalmol 45(1):78
Article CAS PubMed Google Scholar
Govindharaj I, Santhakumar D, Pugazharasi K, Ravichandran S, Prabhu RV, Raja J (2025) Enhancing glaucoma diagnosis: generative adversarial networks in synthesized imagery and classification with pretrained MobileNetV2. MethodsX 14:103116
Article CAS PubMed Google Scholar
Govindharaj I, Ramesh T, Poongodai A, Udayasankaran P, Ravichandran S (2025) Grey wolf optimization technique with U-shaped and capsule networks-a novel framework for glaucoma diagnosis. MethodsX 14:103285
Govindharaj I, Poongodai A, Santhakumar D, Ravichandran S, Vijaya Prabhu R, Udayakumar K, Yazhinian S (2025) Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning. MethodsX 14:103052
Govindharaj I, Anandh R, KP S, Shanmugam M, Yazhinian S (2025) A Dilated Feature Fusion Approach to Optic Cup and Disc Segmentation for Glaucoma Detection and Progression Prediction. In2025 International Conference on Emerging Technologies in Engineering Applications (ICETEA) 1–6. IEEE.
Caraffa L, Tarel JP, Charbonnier P (2015) The guided bilateral filter: when the joint/cross bilateral filter becomes robust. IEEE Trans Image Process 24(4):1199–1208
D’Souza G, Siddalingaswamy PC, Pandya MA (2024) Alternet-K: a small and compact model for the detection of glaucoma. Biomed Eng Lett 14(1):23–33
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. InProceedings of the IEEE conference on computer vision and pattern recognition. 770–778.
Chan K, Lee TW, Sample PA, Goldbaum MH, Weinreb RN, Sejnowski TJ (2002) Comparison of machine learning and traditional classifiers in glaucoma diagnosis. IEEE Trans Biomed Eng 49(9):963–974
Dey A, Bandyopadhyay S (2016) Automated glaucoma detection using support vector machine classification method. Br J Med Med Res 11(12):1–2
Alice K, Deepa N, Devi T, BeenaRani BB, Bharatha Devi N, Nagaraju V (2023) Effect of multi filters in glucoma detection using random forest classifier. Measurement: Sensors. 25:100566.
Al-Bander B, Al-Nuaimy W, Al-Taee MA, Zheng Y (2017) Automated glaucoma diagnosis using deep learning approach. In2017 14th International Multi-Conference on Systems, Signals & Devices (SSD) 207–210. IEEE.
Ajitha S, Judy MV (2020) Faster R-CNN classification for the recognition of glaucoma. InJournal of Physics: Conference Series. 1706(1) 012170. IOP Publishing.
Ovreiu S, Paraschiv EA, Ovreiu E (2021) Deep learning & digital fundus images: Glaucoma detection using DenseNet. In2021 13th international conference on electronics, computers and artificial intelligence (ECAI) 1–4. IEEE.
Lenka S, Rout AK, Kumar A, Sahay YR, Lazarus MZ (2024) Vision transformer based model for multiclass glaucoma classification. InInternational Conference on Generative Artificial Intelligence, Cryptography, and Predictive Analytics. 293–305. Singapore: Springer Nature Singapore.
Liu Q, Li N, Jia H, Qi Q, Abualigah L (2022) Modified remora optimization algorithm for global optimization and multilevel thresholding image segmentation. Mathematics 10(7):1014
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