Report
Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of Eye
| العنوان: | Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of Eye |
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| المؤلفون: | Dey, Shramana, Dutta, Pallabi, Bhattacharyya, Riddhasree, Pal, Surochita, Mitra, Sushmita, Raman, Rajiv |
| سنة النشر: | 2025 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: Accepted at International Conference on Pattern Recognition (ICPR) 2024 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-031-78104-9_9 |
| URL الوصول: | http://arxiv.org/abs/2501.12048 |
| رقم الانضمام: | edsarx.2501.12048 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-031-78104-9_9 |
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