Download PDFOpen PDF in browserDecoding Diabetic Retinopathy: a Visionary Diagnosis ApproachEasyChair Preprint 113557 pages•Date: November 21, 2023AbstractThis Diabetic retinopathy is a leading cause of blindness in people with diabetes. Early detection and treatment of retinopathy can help to prevent blindness. However, manual detection of retinopathy is time- consuming and requires specialized skills. Deep learning is a type of machine learning that can be used to develop automated systems for detecting retinopathy. CNN is a deep learning model that has been shown to be effective for detecting retinopathy in adults and children as it involves the processing of pixel data. This study proposes to develop a deep learning model to detect retinopathy in both children and adults by observing retina images and pre-medical history using Convolutional Neural Network (CNN). The model will be trained on a dataset of retina images and pre-medical history data from children and adults with and without retinopathy. The model will then be evaluated on a separate dataset of retina images and pre-medical history data to measure its accuracy. Our main task will be to classify the images based on the levels of severity (i.e.) 0 – No DR, 1 – Mild, 2 – Moderate, 3 – Severe, 4 – Proliferative DR Keyphrases: CNN, Diabetic Retinopathy, Levels of severity
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