ArticleIndian journal of ophthalmology2021
Identification of glaucoma from fundus images using deep learning techniques.
Article in Indian journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Leading Towards a Translation Readiness Framework: A Systematic Review of Deep Learning Approaches for Eye Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2026Review
- Brain tumor classification using fractional Laplacian image enhancement and vision transformer.Scientific reports · 2026Article
- Topo-CNN: Retinal Image Analysis with Topological Deep Learning.Journal of imaging informatics in medicine · 2026Article
- HOG-CNN: Integrating Histogram of Oriented Gradients with Convolutional Neural Networks for Retinal Image Classification.Journal of imaging informatics in medicine · 2026Article
- Multimodal Deep Learning Differentiates Papilledema and Non-Arteritic Anterior Ischemic Optic Neuropathy From Healthy Eyes.Investigative ophthalmology & visual science · 2026Article
- Code-Free Machine Learning for the Detection of Common Ophthalmic Diseases.Translational vision science & technology · 2025Article
- [Screening recommendations for glaucoma, age-related macular degeneration and diabetic eye diseases].Die Ophthalmologie · 2025Review
- Artificial intelligence virtual assistants in primary eye care practice.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- AlterNet-K: a small and compact model for the detection of glaucoma.Biomedical engineering letters · 2024Article
- Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data.Translational vision science & technology · 2023Article
- Artificial intelligence and machine learning in ophthalmology: A review.Indian journal of ophthalmology · 2023Review
- Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier fusion.Frontiers in physiology · 2023Article
- A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration.Bioengineering (Basel, Switzerland) · 2022Article
- Comparison between Deep-Learning-Based Ultra-Wide-Field Fundus Imaging and True-Colour Confocal Scanning for Diagnosing Glaucoma.Journal of clinical medicine · 2022Article
- Commentary: Is human supervision needed for artificial intelligence?Indian journal of ophthalmology · 2022Article
- Evaluating a Foundation Artificial Intelligence Model for Glaucoma Detection Using Color Fundus Photographs.Ophthalmology scienceArticle
- OpthaNet: Attention-Integrated Architecture for High-Precision Multi-Class Ophthalmic Image Classification.Healthcare technology lettersArticle
- Advancing glaucoma detection with convolutional neural networks: a paradigm shift in ophthalmology.Romanian journal of ophthalmologyReview
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeGlaucoma is one of the preeminent causes of incurable visual disability and blindness across the world due to elevated intraocular pressure within the eyes. Accurate and timely diagnosis is essential for preventing visual disability. Manual detection of glaucoma is a challenging task that needs expertise and years of experience.
methodsIn this paper, we suggest a powerful and accurate algorithm using a convolutional neural network (CNN) for the automatic diagnosis of glaucoma. In this work, 1113 fundus images consisting of 660 normal and 453 glaucomatous images from four databases have been used for the diagnosis of glaucoma. A 13-layer CNN is potently trained from this dataset to mine vital features, and these features are classified into either glaucomatous or normal class during testing. The proposed algorithm is implemented in Google Colab, which made the task straightforward without spending hours installing the environment and supporting libraries. To evaluate the effectiveness of our algorithm, the dataset is divided into 70% for training, 20% for validation, and the remaining 10% utilized for testing. The training images are augmented to 12012 fundus images.
resultsOur model with SoftMax classifier achieved an accuracy of 93.86%, sensitivity of 85.42%, specificity of 100%, and precision of 100%. In contrast, the model with the SVM classifier achieved accuracy, sensitivity, specificity, and precision of 95.61, 89.58, 100, and 100%, respectively.
conclusionThese results demonstrate the ability of the deep learning model to identify glaucoma from fundus images and suggest that the proposed system can help ophthalmologists in a fast, accurate, and reliable diagnosis of glaucoma.
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