Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Breast Cancer Classification from Histopathological Images with
  Inception Recurrent Residual Convolutional Neural Network   The Deep Convolutional Neural Network (DCNN) is one of the most powerful and
successful deep learning approaches. DCNNs have already provided superior
performance in different modalities of medical imaging including breast cancer
classification, segmentation, and detection. Breast cancer is one of the most
common and dangerous cancers impacting women worldwide. In this paper, we have
proposed a method for breast cancer classification with the Inception Recurrent
Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful
DCNN model that combines the strength of the Inception Network (Inception-v4),
the Residual Network (ResNet), and the Recurrent Convolutional Neural Network
(RCNN). The IRRCNN shows superior performance against equivalent Inception
Networks, Residual Networks, and RCNNs for object recognition tasks. In this
paper, the IRRCNN approach is applied for breast cancer classification on two
publicly available datasets including BreakHis and Breast Cancer Classification
Challenge 2015. The experimental results are compared against the existing
machine learning and deep learning-based approaches with respect to
image-based, patch-based, image-level, and patient-level classification. The
IRRCNN model provides superior classification performance in terms of
sensitivity, Area Under the Curve (AUC), the ROC curve, and global accuracy
compared to existing approaches for both datasets.

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The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics