Retinal Image Segmentation and Disease Classification using Deep Learning

Abstract

Abstract: Segmenting and classifying retinal blood vessels are essential ophthalmology tasks because they reveal important details regarding the health and condition of the retina and its blood vessels. However, there is still room for improvement in terms of accuracy and robustness. Deep learning models have demonstrated significant promise in precisely and quickly segmenting and categorizing retinal pictures. In this study, we suggest the Residual U-Net (ResUNet) model for segmenting retinal arteries and the Resnet50 model for classifying retinal arteries. For enhanced feature extraction and picture segmentation, the ResUNet model, a deep learning architecture, combines the benefits of the U-Net and ResNet50 models. Accuracy was required for segmentation and classification, which gave our study's focus on the project and potential survey results in its distinctiveness. The DRIVE, CHASE-DB1, STARE, and HRF datasets, which include retinal pictures with ground truth annotations, were used to train and test our model. These data were the resource for obtaining the segmented output with analysis in accordance with the initial input supplied to the model. Another sign of the robustness of our model is its strong generalization capacity. Overall, our study shows the usefulness of the ResUNet model for retinal vascular segmentation and classification and highlights its potential for enhancing the diagnosis and treatment of retinal illnesses, which has the potential to improve further with the potential for further development.