Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Densely Connected Recurrent Residual (Dense R2UNet) Convolutional Neural
  Network for Segmentation of Lung CT Images   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.

29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg