Recurrent residual U-Net with EfficientNet encoder for medical image segmentation

In this paper, we propose a U-net architecture that integrates a residual skip connections and recurrent feedback with EfficientNet as a pretrained encoder. Residual connections help feature propagation in deep neural networks and significantly improve performance against networks with a similar number of parameters while recurrent connections ameliorate gradient learning. We also propose a second model that utilizes densely connected layers aiding deeper neural networks. EfficientNet is a family of powerful pretrained encoders that streamline neural network design. The proposed networks are evaluated against state-of-the-art deep learning based segmentation techniques to demonstrate their superior performance.

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