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A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy

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Abstract

The increasing number of diabetic retinopathy (DR) cases world wide demands the development of an automated decision support system for quick and cost-effective screening of DR. We present an automatic screening system for detecting the early stage of DR, which is known as non-proliferative diabetic retinopathy (NPDR). The proposed system involves processing of fundus images for extraction of abnormal signs, such as hard exudates, cotton wool spots, and large plaque of hard exudates. A rule based classifier is used for classifying the DR into two classes, namely, normal and abnormal. The abnormal NPDR is further classified into three levels, namely, mild, moderate, and severe. To evaluate the performance of the proposed decision support framework, the algorithms have been tested on the images of STARE database. The results obtained from this study show that the proposed system can detect the bright lesions with an average accuracy of about 97%. The study further shows promising results in classifying the bright lesions correctly according to NPDR severity levels.

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Acknowledgment

This research work is supported by E-Science Project (No: 01-02-01-SF0025) sponsored by Ministry of Science, Technology and Innovation (MOSTI), Malaysia.

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Authors and Affiliations

  1. Faculty of Engineering, Department of Electrical Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Ahmed Wasif Reza

  2. Faculty of Information Technology, Multimedia University, 63100, Cyberjaya, Malaysia

    C. Eswaran

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  1. Ahmed Wasif Reza
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  2. C. Eswaran
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Correspondence to Ahmed Wasif Reza.

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Reza, A.W., Eswaran, C. A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy. J Med Syst 35, 17–24 (2011). https://doi.org/10.1007/s10916-009-9337-y

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