Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of Eye

• URL: http://arxiv.org/abs/2501.12048v1
• Date: Tue, 21 Jan 2025 11:21:16 GMT
• Title: Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of Eye
• Authors: Shramana Dey, Pallabi Dutta, Riddhasree Bhattacharyya, Surochita Pal, Sushmita Mitra, Rajiv Raman,
• Abstract summary: Early detection and timely intervention are crucial for averting visual impairment and enhancing patient prognosis. This research introduces a new framework called Class Extension with Limited Data (CELD) to train a classifier to categorize retinal fundus images. We achieve an overall accuracy of 91% on publicly available datasets.
• Score: 0.11122957631582182
• License:
• Abstract: The prevalence of ocular illnesses is growing globally, presenting a substantial public health challenge. Early detection and timely intervention are crucial for averting visual impairment and enhancing patient prognosis. This research introduces a new framework called Class Extension with Limited Data (CELD) to train a classifier to categorize retinal fundus images. The classifier is initially trained to identify relevant features concerning Healthy and Diabetic Retinopathy (DR) classes and later fine-tuned to adapt to the task of classifying the input images into three classes: Healthy, DR, and Glaucoma. This strategy allows the model to gradually enhance its classification capabilities, which is beneficial in situations where there are only a limited number of labeled datasets available. Perturbation methods are also used to identify the input image characteristics responsible for influencing the models decision-making process. We achieve an overall accuracy of 91% on publicly available datasets.

Related papers

• Enhancing Retinal Disease Classification from OCTA Images via Active Learning Techniques [0.8035416719640156]
  Eye diseases are common in older Americans and can lead to decreased vision and blindness. Recent advancements in imaging technologies allow clinicians to capture high-quality images of the retinal blood vessels via Optical Coherence Tomography Angiography ( OCTA) OCTA provides detailed vascular imaging as compared to the solely structural information obtained by common OCT imaging.
  arXiv  Detail & Related papers  (2024-07-21T23:24:49Z)
• Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection [0.0]
  Diabetic Retinopathy (DR) is a significant cause of blindness globally, highlighting the urgent need for early detection and effective treatment. Recent advancements in Machine Learning (ML) techniques have shown promise in DR detection, but the availability of labeled data often limits their performance. This research proposes a novel Semi-Supervised Graph Learning SSGL algorithm tailored for DR detection.
  arXiv  Detail & Related papers  (2023-09-02T04:42:08Z)
• Self-Supervised Learning from Unlabeled Fundus Photographs Improves Segmentation of the Retina [4.815051667870375]
  Fundus photography is the primary method for retinal imaging and essential for diabetic retinopathy prevention. Current segmentation methods are not robust towards the diversity in imaging conditions and pathologies typical for real-world clinical applications. We utilize contrastive self-supervised learning to exploit the large variety of unlabeled fundus images in the publicly available EyePACS dataset.
  arXiv  Detail & Related papers  (2021-08-05T18:02:56Z)
• Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images [2.498907460918493]
  We propose a self-supervised framework, namely lesion-based contrastive learning for automated diabetic retinopathy grading. Our proposed framework performs outstandingly on DR grading in terms of both linear evaluation and transfer capacity evaluation.
  arXiv  Detail & Related papers  (2021-07-17T16:30:30Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images [20.480034690570196]
  We propose a straightforward approach to enforce the constraint for the task of predicting Diabetic Retinopathy (DR) severity from eye fundus images. We expand standard classification losses with an extra term that acts as a regularizer. We show how to adapt our method to the modelling of label noise in each of the sub-problems associated to DR grading.
  arXiv  Detail & Related papers  (2020-10-01T10:42:06Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)
• 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)
• 1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions [79.71065005161566]
  1-D convolutional neural network models are trained for classification of short-range sequences. Model provides prediction with high average accuracy on a hold out test set.
  arXiv  Detail & Related papers  (2020-02-06T17:25:37Z)

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.