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Eye Diseases Classification
Chronic Disease & Rehabilitation Management
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About
Retinal images are categorized into four distinct classes: Normal, Diabetic Retinopathy, Cataract, and Glaucoma. This collection encompasses images sourced from multiple reputable datasets, including IDRiD, Ocular Recognition, and HRF.
Features:
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Cataract Images:
Contains 1,038 retinal images labeled as Cataract. Cataracts cause clouding in the eye’s lens, leading to blurred vision. These images provide data that can help train models to identify characteristic signs of cataracts. -
Diabetic Retinopathy Images:
Includes 1,098 retinal images labeled as Diabetic Retinopathy. Diabetic Retinopathy is a complication of diabetes that affects the retina due to damaged blood vessels, potentially leading to blindness. The images capture various stages of retinopathy, which aids in distinguishing its progression. -
Glaucoma Images:
Contains 1,007 images classified as Glaucoma. Glaucoma involves damage to the optic nerve, often due to high intraocular pressure, and can lead to vision loss if untreated. These images highlight signs of optic nerve damage, helping models detect early signs of glaucoma. -
Normal Retinal Images:
Contains 1,074 images labeled as Normal, representing healthy retinal images without signs of disease. These provide a baseline for comparison and are essential for models to distinguish between healthy and diseased retinal conditions.
Usage:
This dataset is ideal for developing and training image classification models for detecting eye diseases. It can be used in deep learning tasks such as Convolutional Neural Networks (CNNs) to automatically identify and categorize retinal conditions.
Coverage:
The dataset covers four common eye conditions, focusing on retinal images, providing a diverse range of data for healthcare-related research and diagnostic applications.
License:
CC0 – Public Domain Dedication.
Who can use it:
- Researchers in healthcare and AI.
- Medical students or professionals interested in medical imaging.
- Data scientists and machine learning practitioners developing disease detection models.
How to use it:
The dataset could be used to:
- Train supervised machine learning models for eye disease detection.
- Develop image classification algorithms to identify the presence of Cataract, Diabetic Retinopathy, Glaucoma, or classify a healthy retina.
- Create healthcare tools to assist ophthalmologists in diagnosing eye conditions based on retinal images.
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