Automatic Screening and Classification of Diabetic Retinopathy Fundus Images

Sarni Suhaila Rahim; Vasile Palade; James Shuttleworth; Chrisina Jayne

doi:10.1007/978-3-319-11071-4_11

Automatic Screening and Classification of Diabetic Retinopathy Fundus Images

Sarni Suhaila Rahim, Vasile Palade, James Shuttleworth, Chrisina Jayne

School of Computing, Electronics and Maths

Research output: Contribution to journal › Article

17 Citations (Scopus)

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.

Original language	English
Pages (from-to)	113-122
Journal	Engineering Applications of Neural Networks: Communications in Computer and Information Science
Volume	459
DOIs	https://doi.org/10.1007/978-3-319-11071-4_11
Publication status	Published - Sep 2014

Bibliographical note

This 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

Keywords

Classifiers
Diabetic Retinopathy
Eye Fundus Images
Eye Screening
Image Processing

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Rahim, S. S., Palade, V., Shuttleworth, J., & Jayne, C. (2014). Automatic Screening and Classification of Diabetic Retinopathy Fundus Images. Engineering Applications of Neural Networks: Communications in Computer and Information Science, 459, 113-122. https://doi.org/10.1007/978-3-319-11071-4_11

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