Optiretina: An Efficiencent Approach For Diabeticretinopathy Classification Using Convolutional Neural Networks

Abstract: One of the complications of Diabetes Mellitus, namely Diabetic Retinopathy (DR) damages the retina of the eye and has five levels of severity: Normal, Mild, Medium, Severe and Proliferate. If not detected and treated, this complication can lead to blindness. Detection and classification of this disease is still done manually by an ophthalmologist using an image of the patient's eye fundus. Manual detection has the disadvantage that it requires an expert in the field and the process is difficult. This research was conducted by detecting and classifying DR disease using Convolutional Neural Network (CNN). The CNN model was built based on the VGG-16 architecture to study the characteristics of the eye fundus images of DR patients. The planned technique is carried out through completely different steps: information collection, pre-processing, augmentation and modelling. The main goal of this work is to develop a robust system for automatic detection of diabetic retinography. Recently, deep learning has become one of the most common techniques that has achieved higher performance in several fields, especially within the analysis and classification of medical pictures. Convolutional neural networks are widely used as a deep learning method in medical image analysis and are highly effective. For this text, ways for detecting and classifying diabetic retinography background color images using deep learning techniques were reviewed and analyzed. Moreover, out there diabetic retinography datasets were reviewed for retinal color fundus. difficult differences that need a lot of investigation are discussed.

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