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Detection and Classification of Diabetic Retinopathy Using Image Processing Algorithms, Convolutional Neural Network, and Signal Processing Techniques
Abstract
Diabetic retinopathy (DR) affects blood vessels in the retina and arises due to complications of diabetes. Diabetes is a serious health issue that must be considered and taken care of at the right time. Modern lifestyle, stress at workplaces, and unhealthy food habits affect the health conditions of our body. So the detection of lesions and treatment at an early stage is required. The detection and classification of early signs of diabetic retinopathy can be done by three different approaches. In Approach 1, an image processing algorithm is proposed. In Approach 2, convolutional neural network (CNN-VGG Net 16) is proposed for the classification of fundus images into normal and DR images. In Approach 3, a signal processing method is used for the detection of diabetic retinopathy using electro retinogram signal (ERG). Finally, the performance measures are calculated for all three approaches, and it is found that detection using CNN improves the accuracy.
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