An Efficient Retinal Segmentation-Based Deep Learning Framework for Disease Prediction

Abstract

Deep learning (DL) technology has shown to be the most effective method of completing class assignments in the last several years. Specifically, these approaches were used for segmentation, classification, and prediction of retinal blood vessels, which was previously unattainable. U-Net deep learning technology has been hailed as one of the most significant technological advances in recent history. In the proposed work, improved segmentation of retinal images using U-Net, bidirectional ConvLSTM U-Net (BiDCU-Net), and fully connected convolutional layers, such as absolute U-Net, BiConvLSTM preferences, and also the fully connected convolutional layer method are proposed. Three well-known datasets were subjected to the suggested technique’s evaluation: the DRIVE, STARE, and CHASE DB1 databases. This suggested technique was tested using the required precise measures in percentage of accuracy, F1 score, sensitivity, and specificity in DRIVE, 97.32, 83.85, 82.56, and 98.68 in CHASE, 97.44, 81.94, 83.92, and 98.45 in STARE, 97.33, 82.3, 82.12, and 98.57 in STARE, respectively. Furthermore, we assert that the strategy outperforms three other similar strategies in terms of effectiveness.

CMP-UNet: A Retinal Vessel Segmentation Network Based on Multi-Scale Feature Fusion

“…Although there are many automatic retinal vessel segmentation models [34], most of the methods still have shortcomings in segmenting fine blood vessels and ensuring vascular connectivity. The shortcomings are mainly due to the following reasons: (1) Fundus images contain rich capillaries with large variations in width.…”