Glaucoma Detection

Latest papers

19 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Cross-Dataset Generalization of Glaucoma Detection CNNs: Effects of Operating Threshold, Multi-Source Training, and Color Normalization
Nov 1, 2026Al-Amin Rahman, Saifuddin Ahmed, Resalat AhmedCross-Dataset Generalization Glaucoma Detection
Department of Electrical and Electronic Engineering, Sylhet Engineering College, Sylhet, Bangladesh (Affiliated with Shahjalal University of Science & Technology, Sylhet)
Detecting Glaucoma Across Multi-ethnic Myopic and Non-Myopic Populations Using an Uncertainty-Aware Vision Transformer: A Multicentre Model Development and Validation Study
Sep 24, 2026Raghavan Lavanya, Yangqin Feng, Ten Cheer Quek +36Fundus Imaging Glaucoma Detection
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore · Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore · Institute of Advanced Intelligence and Computing, Agency for Science, Technology and Research, Singapore +14
Interpretable AI plus Handheld, Portable Retinal Photographs: A Low-Cost Glaucoma Screening Solution for West Africa
Sep 22, 2026Charis Y. N. Chiang, Tarela Sarimiye, Adeyinka Ashaye +4Fundus Imaging Medical Diagnosis
Department of Ophthalmology, Emory University, Atlanta, Georgia, United States · Department of Biomedical Engineering, National University of Singapore, Singapore · Singapore Eye Research Institute, Singapore National Eye Centre, Singapore +5
Attention-Enhanced Deep Features with Heterogeneous Ensemble Learning for Glaucoma Detection
Sep 6, 2026Abdullah Al Shafi, Nishat Sadaf Lira, Abrar Hasan +2Convolutional Block Attention Module Glaucoma Detection
Department of CSE, Khulna University of Engineering & Technology, Bangladesh · Department of CSE, Daffodil International University, Bangladesh · Department of SWE, Green University of Bangladesh
An Agentic AI Framework Overcomes Fundamental Limitations of Large Language Models for Glaucoma Detection from Fundus Photography
Aug 7, 2026Jalil Jalili, Hossein Taghizad, Anuwat Jiravarnsirikul +8Tool-Using Vision-Language Agents Glaucoma Detection
Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA. · Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA · Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivières, Trois-Rivières, QC, Canada +2
Balanced Soft mixture-of-expert model for Glaucoma Detection
Jul 28, 2026Sai Venkatesh Chilukoti, Krishna Rauniyar, Min Shi +1Multimodal Classification Mixture-of-Experts Models
School of Computing and Informatics, University of Louisiana at Lafayette, 301 East Lewis Street, Lafayette, 70504, LA, USA.
GlaKG: A Biomarker-Centric Fundus Knowledge Graph for Explainable Glaucoma Diagnosis and Risk Assessment
Jul 6, 2026Cheng Huang, Jia Zhang, Yi Jiang +7Medical Image Analysis Medical Diagnosis
University of Texas Southwestern Medical Center · 2Southern Methodist University · 3Nanyang Technological University +1
An Automated Multimodal Glaucoma Detection Framework Using ViT and a Stacking-Based Ensemble
Jul 2, 2026Ishrat Jahan, Muhammad E. H Chowdhury, Murugappan Murugappan +7Stacked Ensemble Learning Multimodal Classification
Department of Computer Science and Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh · Department of Electrical Engineering, Qatar University, Doha, Qatar · Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha, Kuwait +5
Ellipse Meets Bit-Planes: A Novel Approach to RNFL based Glaucoma Detection Using Advanced Image Processing and Deep Learning
Jun 14, 2026Snigdha Paul, Sambit Mallick, Anindya SenMedical Image Analysis Fundus Imaging
Electronics and Communication Engineering, Heritage Institute of Technology, Kolkata, 700107, West Bengal, India
DiffSight-Former: Modeling Structural Differences and Temporal Dynamics for Glaucoma Progression Prediction
Jun 8, 2026Yi Huang, Lei Bi, Jinman KimLongitudinal Medical Image Analysis Clinical Outcome Prediction
School of Computer Science, The University of Sydney · Institute of Translational Medicine, Shanghai Jiao Tong University
MPD $^2$ -Router: Mask-aware Multi-expert Prior-regularized Dual-head Deferral Router in Glaucoma Screening and Diagnosis
May 8, 2026Wenxin ZhanAI-Assisted Decision Making Learning to Defer
Baruch college
FairEnc: A Fair Vision-Language Model with Fair Vision and Text Encoders for Glaucoma Detection
May 6, 2026Mohamed Elhabebe, Ayman El-Baz, Qing LiuAlgorithmic Fairness Algorithmic Bias
Center for Machine Vision and Signal Analysis, University of Oulu, Finland · Department of Bioengineering, University of Louisville, USA · The Department of Physics and Engineering, UiT The Arctic University of Norway, Norway
Validating a Deep Learning Algorithm to Identify Patients with Glaucoma using Systemic Electronic Health Records
Apr 22, 2026John Xiang, Rohith Ravindranath, Sophia Y. WangClinical Risk Prediction Glaucoma Detection
Department of Ophthalmology, Byers Eye Institute, Stanford University, Stanford, California, USA
Fundus Image-based Glaucoma Screening via Retinal Knowledge-Oriented Dynamic Multi-Level Feature Integration
Apr 14, 2026Chi Liu, Yuzhuo Zhou, Sheng Shen +9Glaucoma Detection Medical Image Analysis
Faculty of Data Science, City University of Macau, Avenida Padre Tomás Pereira, Taipa, 999078, Macao, China · State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, 510000, Guangdong, China · Hainan International College, Minzu University of China, Li’an International Education Innovation Pilot Zone, Lingshui, 572423, Hainan, China +4
Nested Radially Monotone Polar Occupancy Estimation: Clinically-Grounded Optic Disc and Cup Segmentation for Glaucoma Screening
Apr 10, 2026Rimsa Goperma, Rojan Basnet, Liang ZhaoOptic Disc Segmentation Glaucoma Detection
Graduate School of Advanced Integrated Studies in Human Survivability (GSAIS), Kyoto University, Kyoto 606-8306, Japan
Retinal Layer Segmentation in OCT Images With 2.5D Cross-slice Feature Fusion Module for Glaucoma Assessment
Mar 25, 2026Hyunwoo Kim, Heesuk Kim, Wungrak Choi +1Glaucoma Detection Medical Image Segmentation
Department of Mechanical Engineering, Yonsei University, Seoul, 03722, South Korea · Institute of Vision Research, Department of Ophthalmology, Severance Hospital, Yonsei University, Seoul, 03722, South Korea
Joint Segmentation and Grading with Iterative Optimization for Multimodal Glaucoma Diagnosis
Mar 15, 2026Zhiwei Wang, Yuxing Li, Meilu Zhu +2Optic Disc Segmentation Glaucoma Detection
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China · College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
GLEAM: A Multimodal Imaging Dataset and HAMM for Glaucoma Classification
Mar 13, 2026Jiao Wang, Chi Liu, Yiying Zhang +8Glaucoma Detection Ophthalmic Image Analysis
College of the Information Science and Engineering, Northeastern University, Shenyang 110819, China · Department of Ophthalmology, Shenyang Fourth People’s Hospital, Shenyang 110000, China
GlaBoost: A Multimodal Structured Framework for Glaucoma Risk Stratification
Aug 3, 2025Cheng Huang, Zeyu Han, Weizheng Xie +4Multimodal Fusion Clinical Risk Prediction
Department of Computer Science, Southern Methodist University, Dallas, TX 75205, USA · Department of Ophthalmology, UT Southwestern Medical Center, Dallas, TX 75390, USA · High Performance Computing Program, National Aeronautics and Space Administration, Washington, DC 20546, USA

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Cross-Dataset Generalization of Glaucoma Detection CNNs: Effects of Operating Threshold, Multi-Source Training, and Color Normalization

Al-Amin Rahman, Saifuddin Ahmed, Resalat Ahmed

Department of Electrical and Electronic Engineering, Sylhet Engineering College, Sylhet, Bangladesh (Affiliated with Shahjalal University of Science & Technology, Sylhet)

Cross-Dataset Generalization Glaucoma Detection Classification Fundus Imaging

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Deep learning models for glaucoma detection are usually tested on images from the same dataset used for training. That says little about how they will work at a new hospital or with a new camera. We fine-tuned five ImageNet-pretrained CNNs (ResNet50, DenseNet121, MobileNetV2, EfficientNet-B0, InceptionV3) with identical settings on one of three public fundus datasets (ACRIMA, ORIGA, RIM-ONE DL). Each was tested on a held-out part of its own dataset and on the other two datasets in full. We used three random seeds and report mean and SD over seeds. Mean in-domain AUC was 0.913 and cross-source AUC was 0.702, a drop of 0.211 (SD 0.030; 95% CI 0.135 to 0.286). After correction for multiple comparisons we found no evidence that any architecture did better, though three seeds give low power. At thresholds set on the source validation data, specificity fell from 0.870 to 0.506 and sensitivity from 0.831 to 0.726. Resetting the threshold on 10% of the target images raised specificity to 0.670 but lowered sensitivity to 0.609, and balanced accuracy rose only from 0.616 to 0.639. Training on two sources together did not help (held-out AUC 0.695 versus 0.702). LAB reference normalization (cross-source AUC 0.755) and strong color augmentation (0.754) gave similar gains, cutting the drop to about 0.16. Ben Graham normalization (0.733) and CLAHE (0.712) helped less, and gray-world (0.696) did not help. The two best methods raised specificity but lowered sensitivity. On G1020, used only for testing, AUC stayed near 0.5 in every condition, and networks trained within G1020 were also close to chance. Strong in-domain results do not show that a glaucoma model will work on new data. Normalization and color augmentation reduce the drop but do not remove it. Studies should report external validation and check the operating threshold, not only AUC.