<![CDATA[Diabetic Retinopathy (DR) is one of the causes of diabetes mellitus and is an important cause of visual disability and blindness. Screening of diabetic retinopathy is essential for both early detection and early treatment. Currently, the ophthalmologists use a non-mydriatic fundus camera to capture retinal images. Based on the fundus images, the ophthalmologists diagnose manually, which is time-consuming and prone to errors. The objectives of this project are to study image processing techniques, particularly on fundus images for diabetic retinopathy screening, to develop an automatic screening and classification system for diabetic retinopathy using fundus images in order to detect diabetic retinopathy at an early stage, and finally, to propose use of new eye fundus images, expert diagnosis image processing techniques, machine learning classifiers, and also App Designer as the Graphical User Interface (GUI) environment for early detection of the signs of diabetic retinopathy. An accurate retinal screening, therefore, is required to assist the retinal screeners to classify the retinal images effectively. Highly efficient and accurate image processing techniques must thus be used in order to produce an effective screening of diabetic retinopathy. It is envisaged that the proposed decision support system for clinical screening would greatly contribute to and assist the management and the detection of diabetic retinopathy.]]>
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