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Detection and Classification of Hard Exudates with Fundus Images Complements and Neural Networks

Diabetic Retinopathy (DR) is an eye disorder that progressively leads to vision loss due to high glucose causing impairment of retinal blood vessels (BVs). ‘Retinal Bright Lesions’ such as ‘Hard Exudates’ (HEs) are plasma leakages from rapture retinal capillaries. HEs appear as hard, waxy, yellowish deposits from tiny spots to fat patches and signify moderate-severe Non-Proliferative Diabetic Retinopathy (NPDR). This paper proposes a simple, compact and computationally inexpensive technique for detection and classification of HEs using Digital Image Processing Techniques on digital fundus images complements and Artificial Neural Networks (ANN). The proposed technique unfolds through five stages i.e. Pre-processing, coarse detection, optimization, features detection & extraction followed by classification. ‘Speed Up Robust Features’ (SURF) algorithm has been used for features detection & extraction while ‘Feed-Forward Back-propagation’ (FFBP) ANN has been used for classification. The proposed technique has yielded 98.7% ‘Sensitivity’ (SE), 97.5% ‘Specificity’ (SP) and 97.7% ‘Accuracy’ (AC) on ‘DIARETDB1’ fundus images.