Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

A Classification Diagnosis of Cervical Cancer Medical Data Based on Various Artificial Neural Networks

Authors
Yong Qi, Zhijian Zhao, Lizeqing Zhang, Haozhe Liu, Kai Lei
Corresponding Author
Yong Qi
Available Online May 2018.
DOI
10.2991/ncce-18.2018.93How to use a DOI?
Keywords
health care; cervical cancer; machine learning; deep learning; regression.
Abstract

This paper mainly proposed to identify and classify the medical data of cervical cancer using neural network models such as SVM, FNN, KNN and so on. The computer recognition algorithm can overcome the deficiency that artificial identification tends to be affected by cognitive ability, subjective experience and fatigue degree. The model trained under various neural networks with the experts’ manual marked data will obtain a more precise result in the identification process of cervical cancer medical data

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
Series
Advances in Intelligent Systems Research
Publication Date
May 2018
ISBN
978-94-6252-517-7
ISSN
1951-6851
DOI
10.2991/ncce-18.2018.93How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yong Qi
AU  - Zhijian Zhao
AU  - Lizeqing Zhang
AU  - Haozhe Liu
AU  - Kai Lei
PY  - 2018/05
DA  - 2018/05
TI  - A Classification Diagnosis of Cervical Cancer Medical Data Based on Various Artificial Neural Networks
BT  - Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)
PB  - Atlantis Press
SP  - 579
EP  - 582
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.93
DO  - 10.2991/ncce-18.2018.93
ID  - Qi2018/05
ER  -