Abstract
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.
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Rahim, S.S., Palade, V., Shuttleworth, J., Jayne, C. (2014). Automatic Screening and Classification of Diabetic Retinopathy Fundus Images. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_11
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DOI: https://doi.org/10.1007/978-3-319-11071-4_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11070-7
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