Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid
  Transfer Learning   Breast cancer is a common cancer for women. Early detection of breast cancer
can considerably increase the survival rate of women. This paper mainly focuses
on transfer learning process to detect breast cancer. Modified VGG (MVGG),
residual network, mobile network is proposed and implemented in this paper.
DDSM dataset is used in this paper. Experimental results show that our proposed
hybrid transfers learning model (Fusion of MVGG16 and ImageNet) provides an
accuracy of 88.3% where the number of epoch is 15. On the other hand, only
modified VGG 16 architecture (MVGG 16) provides an accuracy 80.8% and MobileNet
provides an accuracy of 77.2%. So, it is clearly stated that the proposed
hybrid pre-trained network outperforms well compared to single architecture.
This architecture can be considered as an effective tool for the radiologists
in order to reduce the false negative and false positive rate. Therefore, the
efficiency of mammography analysis will be improved.

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The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas