Early Glaucoma Detection using Deep Learning with Multiple Datasets of Fundus Images

• URL: http://arxiv.org/abs/2506.21770v1
• Date: Thu, 26 Jun 2025 21:06:51 GMT
• Title: Early Glaucoma Detection using Deep Learning with Multiple Datasets of Fundus Images
• Authors: Rishiraj Paul Chowdhury, Nirmit Shekar Karkera,
• Abstract summary: Glaucoma is a leading cause of irreversible blindness, but early detection can significantly improve treatment outcomes.<n>In this work, we present a deep learning pipeline using the EfficientNet-B0 architecture for glaucoma detection from retinal fundus images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Glaucoma is a leading cause of irreversible blindness, but early detection can significantly improve treatment outcomes. Traditional diagnostic methods are often invasive and require specialized equipment. In this work, we present a deep learning pipeline using the EfficientNet-B0 architecture for glaucoma detection from retinal fundus images. Unlike prior studies that rely on single datasets, we sequentially train and fine-tune our model across ACRIMA, ORIGA, and RIM-ONE datasets to enhance generalization. Our experiments show that minimal preprocessing yields higher AUC-ROC compared to more complex enhancements, and our model demonstrates strong discriminative performance on unseen datasets. The proposed pipeline offers a reproducible and scalable approach to early glaucoma detection, supporting its potential clinical utility.

Related papers

• The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound [60.80780313225093]
  This study systematically investigated the impact of data augmentation and preprocessing strategies in self-supervised learning for lung ultrasound.<n>Three data augmentation pipelines were assessed: a baseline pipeline commonly used across imaging domains, a novel semantic-preserving pipeline designed for ultrasound, and a distilled set of the most effective transformations from both pipelines.
  arXiv  Detail & Related papers  (2025-04-10T16:26:47Z)
• PathSegDiff: Pathology Segmentation using Diffusion model representations [63.20694440934692]
  We propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors.<n>Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images.<n>Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
  arXiv  Detail & Related papers  (2025-04-09T14:58:21Z)
• DiffuPT: Class Imbalance Mitigation for Glaucoma Detection via Diffusion Based Generation and Model Pretraining [1.8218878957822688]
  glaucoma is a progressive optic neuropathy characterized by structural damage to the optic nerve head and functional changes in the visual field.<n>We use a generative-based framework to enhance glaucoma diagnosis, specifically addressing class imbalance through synthetic data generation.
  arXiv  Detail & Related papers  (2024-12-04T17:39:44Z)
• RADNet: Ensemble Model for Robust Glaucoma Classification in Color Fundus Images [0.0]
  Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness. Regular glaucoma screenings of the population shall improve early-stage detection, however the desirable frequency of etymological checkups is often not feasible. In our work, we propose an advanced image pre-processing technique combined with an ensemble of deep classification networks.
  arXiv  Detail & Related papers  (2022-05-25T16:48:00Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma Grading [1.2250035750661867]
  We propose an efficient multi-modality supervised contrastive learning framework, named COROLLA, for glaucoma grading. We employ supervised contrastive learning to increase our models' discriminative power with better convergence. On the GAMMA dataset, our COROLLA framework achieves overwhelming glaucoma grading performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-11T06:00:51Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs [22.863901758361692]
  Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. Various deep learning models have been applied for detecting glaucoma from digital fundus images, due to the scarcity of labeled data. In this study, compact Self-Organized Operational Neural Networks (Self- ONNs) are proposed for early detection of glaucoma in fundus images.
  arXiv  Detail & Related papers  (2021-09-28T10:27:01Z)
• Data augmentation for deep learning based accelerated MRI reconstruction with limited data [46.44703053411933]
  Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks. To achieve state-of-the-art performance, training on large and diverse sets of images is considered critical. We propose a pipeline for data augmentation for accelerated MRI reconstruction and study its effectiveness at reducing the required training data.
  arXiv  Detail & Related papers  (2021-06-28T19:08:46Z)
• Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks [1.1601676598120785]
  Glaucoma is one of the leading causes of blindness worldwide. We propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms.
  arXiv  Detail & Related papers  (2021-06-25T10:53:01Z)
• RetiNerveNet: Using Recursive Deep Learning to Estimate Pointwise 24-2 Visual Field Data based on Retinal Structure [109.33721060718392]
  glaucoma is the leading cause of irreversible blindness in the world, affecting over 70 million people. Due to the Standard Automated Perimetry (SAP) test's innate difficulty and its high test-retest variability, we propose the RetiNerveNet.
  arXiv  Detail & Related papers  (2020-10-15T03:09:08Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.