Download PDFOpen PDF in browserDiabetic Retinopathy Classification Using Deep Learning TechniqueEasyChair Preprint 80186 pages•Date: May 22, 2022AbstractDiabetic Retinopathy is a disease that damages the eyes and is caused by a consequence of diabetes. If blood sugar levels aren't controlled for an extended period of time, the disease can develop. It is mainly caused due to the damage of blood vessels in the retina. Retinopathy is the main cause of blindness in the world. Doctors can diagnose blindness before it occurs using Artificial Intelligence and Deep Learning. Medical imaging plays a very crucial role in a variety of medical issues and at all major levels of health issues. Medical imaging can be used to identify a variety of common eye illnesses. However, for a variety of reasons, including uneven lighting, picture blurring, and low contrast and brightness, poor-quality retinal images are ineffective for further diagnosis, particularly in automated analysing systems. Ophthalmologists' manual Diabetic Retinopathy diagnostic procedure is time-consuming, requires more work, costly, and might result in misdiagnosis. Basing on the vision like having trouble in reading distant objects or seeing distant objects, blindness or any other changes may happen in eye retina that affects diabetes. Diabetic retinopathy is one of the most frequent eye illnesses, affecting mostly diabetics. This model using deep learning convolution neural networks can assist the opthmologists by providing clear images of the retina, and also blood vessel extracted images. There are three phases in this diabetic retinopathy detection and classification technique. These three phases are pre-processing, blood vessel and exudates detection, feature extraction and classification. In this work, From the presented retinal fundus pictures, we utilised the Res-Block model to classify and diagnose diabetic retinopathy with 92% of accuracy. Keyphrases: Artificial Intelligence, Diabetic Retinopathy, Fundus, Medical Imaging, deep learning
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