scholarly journals An Analysis on Cervical Cancer Classification of Medical Digital Images Using Various Classifiers

2019 ◽  
pp. 98-102
Author(s):  
M. Robinson Joel ◽  
G. Vishali ◽  
R. Ponlatha ◽  
Syed Sharmila Begum
Keyword(s):  
Cervical Cancer ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Digital Images ◽  
Great Accuracy ◽  
Reduction Technique ◽  
Gynecological Malignancy ◽  
Dimensionality Reduction Technique ◽  
The World

In this analysis, Cervical cancer took over the place four in the world level and it is the most prevalent cancer that is affecting women. If the cancer is detected in the earlier stages it can be cured and treated successfully. And it is also the leading gynecological malignancy disease worldwide. This is a paper which presents the classification techniques of cervical cancer. And also, this paper shows the advanced feature solution approaches of cervical cancer. The dimensionality reduction technique is used for the improvement of the classifier with great accuracy. There are two categories of feature selection and they are filters and wrappers. By using all these analytic techniques, we can classify cancer and its approaches. Therefore, this paper classifies the approaches of Cervical cancer.

Band Selection Oriented to Easy-Confused Objects for Classification of Hyperspectral Imagery

2012 ◽  
Vol 500 ◽  
pp. 355-361
Author(s):  
Xin Zhao ◽  
Xing Li
Keyword(s):  
Dimensionality Reduction ◽  
Reduction Technique ◽  
Band Selection ◽  
Bhattacharyya Distance ◽  
Hyper Spectral ◽  
Processing Step ◽  
Dimensionality Reduction Technique ◽  
Selection Approach ◽  
Confusion Index

As a dimensionality reduction technique, band selection is an importance pre-processing step for classifiers. In this paper, a band selection approach oriented to easy-confused objects for classification of hyper spectral imagery is presented. Firstly, an Objects Confusion Index (OCI) is established to ascertain the easy-confused objects. Then the two band selection schemes, that are two-class mode and multi-class mode, are designed by adopting Bhattacharyya distance as class reparability measure.

scholarly journals Decision Tree based Classification and Dimensionality Reduction of Cervical Cancer

2020 ◽  
Vol 9 (4) ◽  
pp. 1531-1535
Keyword(s):  
Cervical Cancer ◽  
Decision Tree ◽  
Dimensionality Reduction ◽  
Reduction Technique ◽  
High Dimensional ◽  
Biological Processes ◽  
Decision Tree Algorithm ◽  
Dimensionality Reduction Technique ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree

The data revolution in medicines and biology have increased our fundamental understandings of biological processes and determining the factors causing any disease, but it has also posed a challenge towards their analysis. After breast cancer, most of the deaths among women are due to cervical cancer. According to IARC, alone in 2012 a noticeable number of cases estimated 7095 of cervical cancer were reported. 16.5% of the deaths were due to the cervical cancer with the total deaths of 28,711 among women. To analyze the high dimensional data with high accuracy and in less amount of time, their dimensionality needs to be reduced to remove irrelevant features. The classification is performed using the recent iteration in Quinlan’s C4.5 decision tree algorithm i.e. C5.0 algorithm and PCA as Dimensionality Reduction technique. Our proposed methodology has shown a significant improvement in the account of time taken by both algorithms. This shows that C5.0 algorithm is superior to C4.5 algorithm.

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