Eye Disease Image Dataset

Published: 2 April 2024| Version 1 | DOI: 10.17632/s9bfhswzjb.1


1. Worldwide, eye ailments are recognized as significant contributors to nonfatal disabling conditions. In Bangladesh, 1.5% of adults suffer from blindness, while 21.6% experience low vision. Therefore, eye disease detection is crucial for preserving vision, preventing blindness, and maintaining overall health. Early detection allows for prompt intervention and treatment, preventing irreversible damage and preserving quality of life. By analyzing the dataset, researchers will be able to identify trends, develop algorithms for diagnosis, assess treatment effectiveness, and inform preventive measures. 2. Currently, computer vision methods show great promise in carrying out classification and detection tasks of this nature. 3. To develop computer vision-based algorithms, an extensive eye disease dataset is presented containing original and augmented datasets of a variety of eye diseases such as Retinitis Pigmentosa, Retinal Detachment, Pterygium, Myopia, Macular Scar, Glaucoma, Disc Edema, Diabetic Retinopathy, Central Serous Chorioretinopathy, and Healthy eye image. The classifications of this dataset are done with the help of a domain expert from a healthcare institute. 4. A total of 5335 images of healthy and affected eye images were collected from Anwara Hamida Eye Hospital in Faridpur and BNS Zahrul Haque Eye Hospital in Faridpur district with the help of the hospital authorities. Then from these original images, a total of 16242 augmented images are produced by using Rotation, Width shifting, Height shifting, Translation, Flipping, and Zooming techniques to increase the number of data.



Daffodil International University, Jahangirnagar University


Computer Vision, Medical Imaging, Image Processing, Machine Learning, Deep Learning