A hybrid EfficientNet-R2-ASPP-dual-attention U-Net with Tversky loss reduces missed acute infarcts on DWI relative to common baselines on a 167-patient in-house dataset.
Densely Connected Recurrent Residual (Dense R2UNet) Convolutional Neural Network for Segmentation of Lung CT Images
1 Pith paper cite this work, alongside 6 external citations. Polarity classification is still indexing.
1
Pith paper citing it
6
external citations · Pith
abstract
Deep Learning networks have established themselves as providing state of art performance for semantic segmentation. These techniques are widely applied specifically to medical detection, segmentation and classification. The advent of the U-Net based architecture has become particularly popular for this application. In this paper we present the Dense Recurrent Residual Convolutional Neural Network(Dense R2U CNN) which is a synthesis of Recurrent CNN, Residual Network and Dense Convolutional Network based on the U-Net model architecture. The residual unit helps training deeper network, while the dense recurrent layers enhances feature propagation needed for segmentation. The proposed model tested on the benchmark Lung Lesion dataset showed better performance on segmentation tasks than its equivalent models.
fields
cs.CV 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI
A hybrid EfficientNet-R2-ASPP-dual-attention U-Net with Tversky loss reduces missed acute infarcts on DWI relative to common baselines on a 167-patient in-house dataset.