Authors - A Aruna kumari, Sri Vishnu Prabhu Gudavalli, Tamminana Visweswari Abstract - Diabetic Retinopathy (DR) is one of the biggest contributors of visual impairment and blindness in diabetic patients who do not receive proper measures and treatment at the early stages. Due to the ever rising instances of diabetes by the day, more concern has been raised on the effectiveness of methods of effective, scalable and early diagnosis. The given paper is a proposal of a deep learning-based algorithm of DR diagnosis, specifically, the algorithm named Patch-Wise Segmentation and Classification using Convolutional Neural Networks (CNNs). The system in this case is in contrast to the traditional systems, which require the entire retina image to be processed simultaneously by the system, whereby high-resolution fundus photographs are broken into patches. The model addresses each patch individually in order to have the model closely observe minute-scale details and sensitive pathological changes such as hemorrhages and exudates. Patch-wise processing dramatically enhances the capacity of reporting early and mild cases of DR that are hard to recognize in the fullimage processing due to intricacy of an image and noise. The CNN architecture is additionally medical image particular and operates by use of layers and regularization methods so as to permit not only accuracy but also generalization over a wide range of datasets. As shown in the results of the experiments, the patchwise technique performs better in comparison with the traditional ones, i.e., sensitivity, specificity, and classification accuracy on each of the stages of the DR. In addition, the system is fully automated and stable that minimizes the use of human marking and offers the facility to operate worldwide
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