  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.
