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

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

Authors
Yong Qi, Haozhe Liu, Wentian Zhang, Qiaosheng Zhu, Zhijian Zhao
Corresponding Author
Yong Qi
Available Online May 2018.
DOI
10.2991/ncce-18.2018.91How to use a DOI?
Keywords
health care; liver complaint; machine learning; deep learning; regression.
Abstract

This paper presents a method for the identification and classification of medical data of hepatic pathological changes by using SVM FNN and KNN. The liver lesion classifier is a neural network model trained by experts’ hand-divided samples and cross-validated to optimize the results. It can achieve better identification performance with medical data of hepatic pathological changes by training a variety of different neural network structures

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
10.2991/ncce-18.2018.91
ISSN
1951-6851
DOI
10.2991/ncce-18.2018.91How 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  - Haozhe Liu
AU  - Wentian Zhang
AU  - Qiaosheng Zhu
AU  - Zhijian Zhao
PY  - 2018/05
DA  - 2018/05
TI  - A Classification Diagnosis of Liver 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  - 570
EP  - 573
SN  - 1951-6851
UR  - https://doi.org/10.2991/ncce-18.2018.91
DO  - 10.2991/ncce-18.2018.91
ID  - Qi2018/05
ER  -