Eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents an automatic screening system for diabetic retinopathy to be used in the field of retinal ophthalmology. The paper first explores the existing systems and applications related to diabetic retinopathy screening and detection methods that have been previously reported in the literature. The proposed ophthalmic decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy fundus images, which will assist in the detection and management of the diabetic retinopathy. The developed system contains four main parts, namely the image acquisition, the image preprocessing, the feature extraction, and the classification by using several machine learning techniques.
|Journal||Engineering Applications of Neural Networks: Communications in Computer and Information Science|
|Publication status||Published - Sep 2014|
Bibliographical noteThis paper is not yet available on the repository. The paper was given at the 15th International Conference on Engineering Applications of Neural Networks, EANN 2014; Sofia; Bulgaria; 5 September 2014 through 7 September 2014
- Diabetic Retinopathy
- Eye Fundus Images
- Eye Screening
- Image Processing