Inception v3 based cervical cell classification combined with artificially extracted features

2020 ◽  
Vol 93 ◽  
pp. 106311 ◽  
Author(s):  
N. Dong ◽  
L. Zhao ◽  
C.H. Wu ◽  
J.F. Chang
Keyword(s):  
Cell Classification ◽  
Cervical Cell

A More Comprehensive Cervical Cell Classification Using Convolutional Neural Network

2018 ◽  
Vol 7 (5) ◽  
pp. S66 ◽  
Author(s):  
Vanessa Martin ◽  
Tae Hun Kim ◽  
Melanie Kwon ◽  
Mohammed Kuko ◽  
Mohammad Pourhomayoun ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cell Classification ◽  
Cervical Cell

Triplet Loss based Metric Learning for Cervical Cell Classification

2021 ◽  
Author(s):  
Jianfang Chang ◽  
Na Dong ◽  
Qingyue Feng ◽  
Xinyu Liu
Keyword(s):  
Metric Learning ◽  
Cell Classification ◽  
Cervical Cell ◽  
Triplet Loss

scholarly journals DeepPap: Deep Convolutional Networks for Cervical Cell Classification

2017 ◽  
Vol 21 (6) ◽  
pp. 1633-1643 ◽  
Author(s):  
Ling Zhang ◽  
Le Lu ◽  
Isabella Nogues ◽  
Ronald M. Summers ◽  
Shaoxiong Liu ◽  
...  
Keyword(s):  
Cell Classification ◽  
Convolutional Networks ◽  
Cervical Cell

scholarly journals Estimating false positive and false negative error rates in cervical cell classification.

1977 ◽  
Vol 25 (7) ◽  
pp. 696-701 ◽  
Author(s):  
L H Oliver ◽  
R S Poulsen ◽  
G T Toussaint
Keyword(s):  
False Negative ◽  
Recognition System ◽  
Classification Performance ◽  
Error Rates ◽  
Cell Classification ◽  
Cervical Cell ◽  
Performance Curves ◽  
False Negative Error ◽  
A Cell ◽  
Cervical Cells

The performance of a cell recognition system on unknown data is often estimated in terms of its error rates on a test set. This paper investigates methods for producing estimates of error rates in cervical cell classification. Classification performance curves calculated using these methods are given for several classification schemes used to classify 1500 cervical cells.

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