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The use of deep learning is particularly effective for biomedical applications involving semantic segmentation. In semantic segmentation, one of the most popular deep learning architectures is U-Net, which is specifically designed for feature cascading for pixel classification. There are several versions of U-Net, such as Residual U-Net (ResU-Net), Recurrent U-Net (RU-Net), and Recurrent Residual U-Net (R2U-Net), which have been proposed for improved performance. The recurrent connection in a layer of the neural network can create a cycle of transferring the output information of a layer back to itself as an input. Each layer's output responses can thus be thought of as additional input variables. The new model is based on Residues in Succession U-Net where the residues from successive layers extract reinforced information from the previous layers in addition to the recurrent feedback loop exhibiting several advantages. The improved learning and accumulation of the features in subsequent layers play a major part. The proposed model produces precise extraction and accumulation of features from each layer reinforcing the learning. The outputs of the combination of recurrent and residues in successive layers ensure better feature representation for segmentation tasks. We use a benchmark expert-annotated dataset viz. Structured Analysis of Retina (STARE) for measuring the abilities of the Residues in Succession Recurrent U-Net (RSR U-Net) to segment blood vessels in retinal images. The testing and evaluation results show that the new model provides improved performance when compared to U-Net, R2U-Net and Residues in Succession U-Net in the same experimentation setup.
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