Supervised learning software model for the diagnosis of diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of eye diseases and vision loss for diabetic affected people. Due to the damage of retinal blood vessels, diabetic patients often suffer from DR. So the retinal blood vessel segmentation plays a crucial role in the diagnosis of DR. We can prevent vision loss or blindness problems if the diagnosis happens during the early stages. Early diagnosis and initial investigation would help lower the risk of vision loss by 50%. This article exploits the supervised classification approach to detect blood vessels by applying features such as grey level and invariant moments. The image pre-processing and blood vessel segmentation are the two essential steps are used in this study, along with the proposed classification framework using neural network models. Two publicly available retinal image datasets, such as DRIVE and STARE, are used to assess the proposed supervised classification framework. The suggested supervised classification methodology in this study attains the average retinal blood vessel segmentation accuracy of 93.94% in the DRIVE dataset and 95.00% in the STARE dataset.