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Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation.
TLDR
A Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual convolutional neural Network (RRCNN), which are named RU-Net and R2U-Net respectively are proposed, which show superior performance on segmentation tasks compared to equivalent models including U-nets and residual U- net.Abstract:
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. The proposed models utilize the power of U-Net, Residual Network, as well as RCNN. There are several advantages of these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architecture. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architecture with same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets such as blood vessel segmentation in retina images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models including U-Net and residual U-Net (ResU-Net).read more
Citations
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Journal ArticleDOI
Biomedical Image Segmentation: A Survey
TL;DR: A comprehensive review of the current medical segmentation techniques is presented in this article, where the most important segmentation methods have been utilized for almost all types of medical images and their main advantages and limitations are discussed.
Proceedings ArticleDOI
Recurrent residual U-net with efficientnet encoder for medical image segmentation
TL;DR: A U-net architecture that integrates a residual skip connections and recurrent feedback with EfficientNet as a pretrained encoder is proposed, which is a family of powerful Pretrained encoders that streamline neural network design.
Proceedings ArticleDOI
Enhanced Convolutional Neural Networks for Segmentation of Retinal Blood Vessel Image
Olubunmi Sule,Serestina Viriri +1 more
TL;DR: An Enhanced Deep Convolutional Networks for Segmentation of Retinal Blood Vessel to explore the availability of huge channels and usage of global location and context in the U-net model and achieves the state-of-the-art for segmentation of retinal blood vessels.
Journal ArticleDOI
ResBCU-Net: Deep learning approach for segmentation of skin images
Noor Badshah,Asif Ahmad +1 more
TL;DR: A neural network based on the CNNs for segmentation of medical images is presented, an extension of the U-Net which utilizes Residual blocks, Batch normalization and Bi-directional ConvLSTM, and an extended form of ResBCU-Net, which takes advantage of densely connected layers in its bottleneck section.
Journal ArticleDOI
Transfer learning in deep neural network based under-sampled MR image reconstruction.
TL;DR: Results show successful reconstructions obtained from the proposed method as reflected by the Structural SIMilarity index, Root Mean Square Error, Peak Signal-to-Noise Ratio and central line profile of the reconstructed images.
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