Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Breast cancer detection using artificial intelligence techniques: A
  systematic literature review   Cancer is one of the most dangerous diseases to humans, and yet no permanent
cure has been developed for it. Breast cancer is one of the most common cancer
types. According to the National Breast Cancer foundation, in 2020 alone, more
than 276,000 new cases of invasive breast cancer and more than 48,000
non-invasive cases were diagnosed in the US. To put these figures in
perspective, 64% of these cases are diagnosed early in the disease's cycle,
giving patients a 99% chance of survival. Artificial intelligence and machine
learning have been used effectively in detection and treatment of several
dangerous diseases, helping in early diagnosis and treatment, and thus
increasing the patient's chance of survival. Deep learning has been designed to
analyze the most important features affecting detection and treatment of
serious diseases. For example, breast cancer can be detected using genes or
histopathological imaging. Analysis at the genetic level is very expensive, so
histopathological imaging is the most common approach used to detect breast
cancer. In this research work, we systematically reviewed previous work done on
detection and treatment of breast cancer using genetic sequencing or
histopathological imaging with the help of deep learning and machine learning.
We also provide recommendations to researchers who will work in this field

0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
3
The categories of this paper are: physics
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg