Diagnosing diabetic retinopathy using retinal blood vessel examination based on convolution neural network

Retinal blood vessels include arteries and veins and are usually next to each other. Blood vessels are used to classify the severity of the disease and are also used for guidance during surgery, as retinopathy is one of the dangerous diseases.Diabetic retinopathy can cause the formation of new vessels (neoangiogenesis). This condition causes low vision and even blindness. Therefore, a reliable method for diagnosing and classifying the vessel is needed in order to avoid these complications. Retinopathy is one of the hidden diseases that is usually not known. prevent the next possibility.There are several methods for diagnosis, the most common of which is the use of traditional methods based on manual feature extraction, which requires a lot of feature geometry and expertise, and is usually dependent on data.From this method, neural convolution is a reliable, efficient and reliable method for extracting features without manual intervention, which requires a lot of expertise, which also reduces the dependence on data.In this article, using convolutional neural network, diabetic retinopathy has been diagnosed with accuracy and sensitivity of 98.8% and 97.5%, respectively.The obtained results indicate that the proposed method is suitable for locating blood vessels automatically.