Segmentation of Retinal Blood Vessels Using U-Net++ Architecture and Disease Prediction

This study presents a segmentation method for the blood vessels and provides a method for disease diagnosis in individuals based on retinal images. Blood vessel segmentation in images of the retina is very challenging in medical analysis and diagnosis. It is an essential tool for a wide range of medical diagnoses. After segmentation and binary image improvement operations, the resulting binary images are processed and the features in the blood vessels are used as feature vectors to categorize retinal images and diagnose the type of disease available. To carry out the segmentation task and disease diagnosis, we used a deep learning approach involving a convolutional neural network (CNN) and U-Net++ architecture. A multi-stage method is used in this study to better diagnose the disease using retinal images. Our proposed method includes improving the color image of the retina, applying the Gabor filter to produce images derived from the green channel, segmenting the green channel by receiving images produced from the Gabor filter using U-Net++, extracting HOG and LBP features from binary images, and finally disease diagnosis using a one-dimensional convolutional neural network. The DRIVE and MESSIDOR image banks have been used to segment the image, determine the areas related to blood vessels in the retinal image, and evaluate the proposed method for retinal disease diagnosis. The achieved results for accuracy, sensitivity, specificity, and F1-score are 98.9, 94.1, 98.8, 85.26, and, 98.14, respectively, in the DRIVE dataset and the obtained results for accuracy, sensitivity, and specificity are 98.6, 99, 98, respectively, in MESSIDOR dataset. Hence, the presented system outperforms the manual approach applied by skilled ophthalmologists