Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A survey on deep learning approaches for breast cancer diagnosis   Deep learning has introduced several learning-based methods to recognize
breast tumours and presents high applicability in breast cancer diagnostics. It
has presented itself as a practical installment in Computer-Aided Diagnostic
(CAD) systems to further assist radiologists in diagnostics for different
modalities. A deep learning network trained on images provided by hospitals or
public databases can perform classification, detection, and segmentation of
lesion types. Significant progress has been made in recognizing tumours on 2D
images but recognizing 3D images remains a frontier so far. The interconnection
of deep learning networks between different fields of study help propels
discoveries for more efficient, accurate, and robust networks. In this review
paper, the following topics will be explored: (i) theory and application of
deep learning, (ii) progress of 2D, 2.5D, and 3D CNN approaches in breast
tumour recognition from a performance metric perspective, and (iii) challenges
faced in CNN approaches.

14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom