Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Understand

Deep learning (DL) based semantic segmentation methods have been providing state-of-the-art performance in the last few years.

• More specifically, these techniques have been successfully applied to medical image classification, segmentation, and detection tasks.
• One deep learning technique, U-Net, has become one of the most popular for these applications.
• In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.