Neural Network Technology applied in the Diagnosis of Ovarian Tumors

DOI

10.2991/asei-15.2015.240How to use a DOI?

Keywords

Feature extraction, artifical neural network, Cell recognition

Abstract

The original ovarian cells from ascites are preprocessed and samples are gained in the paper, .Features parameters of morphology are extracted from images of cells samples.The images of cells samples are recognized and classified by Multilayer Perceptron Neural Network and Radial Basis Function Neural Network.Several arithmetics of MLPNN and RBFNN are discussed,and cross entropy arithmetic are suggested.Among the recognized results,the recognition rate and classification of RBFNN and MLPNN with BP arithmetic based on adaptive are the best one.

Copyright

© 2015, 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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TY  - CONF
AU  - Lihong Bao
PY  - 2015/05
DA  - 2015/05
TI  - Neural Network Technology applied in the Diagnosis of Ovarian Tumors
BT  - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
PB  - Atlantis Press
SP  - 1223
EP  - 1226
SN  - 2352-5401
UR  - https://doi.org/10.2991/asei-15.2015.240
DO  - 10.2991/asei-15.2015.240
ID  - Bao2015/05
ER  -