Optical diagnosis of colon and cervical cancer by support vector machine

2016 ◽  
Author(s):  
Sabyasachi Mukhopadhyay ◽  
Indrajit Kurmi ◽  
Rajib Dey ◽  
Nandan K. Das ◽  
Sanjay Pradhan ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Support Vector Machine ◽  
Support Vector ◽  
Optical Diagnosis

Support vector machine for optical diagnosis of cancer

2005 ◽  
Vol 10 (2) ◽  
pp. 024034 ◽  
Author(s):  
S. K. Majumder ◽  
N. Ghosh ◽  
P. K. Gupta
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Optical Diagnosis ◽  
Diagnosis Of Cancer

scholarly journals A Novel Framework for Detection of Cervical Cancer

2018 ◽  
Vol 7 (2) ◽  
pp. 26-30
Author(s):  
V. Pushpalatha
Keyword(s):  
Cervical Cancer ◽  
Support Vector Machine ◽  
Curvelet Transform ◽  
Uterine Cervical Cancer ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Adaptive Support ◽  
Occurrence Matrix ◽  
Five Phases

Today, Uterine Cervical Cancer is most general form of cancer for women. Prevention of cervical cancer is possible via various screening courses. Colposcopy images of cervix are analyzed in this study for the recognition of cervical cancer. An innovative framework is suggested to correctly identify cervical cancer by employing effective pre-processing, image enhancement, and image segmentation techniques. This framework comprises of five phases, (i) Dual tree discrete wavelet transform to pre-process the image (ii) Curvelet transform and contour transform to enhance the image (iii) K-means for segmentation (iv) features computation using Gray level co-occurrence matrix (v) classification using adaptive Support vector machine. The experimental results evident that proposed technique is superior to existing methodologies.

Comparison of Classifier Configurations for the Classification of Cervical Intraepithelial Neoplasia Using Acetic Acid Test Images

2019 ◽  
Vol 9 (6) ◽  
pp. 1103-1111 ◽  
Author(s):  
Jun Liu ◽  
Hongwei Du ◽  
Han Lu ◽  
Yun Peng ◽  
Ling Li ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Support Vector Machine ◽  
Acetic Acid ◽  
Cervical Intraepithelial Neoplasia ◽  
Screening Tool ◽  
Intraepithelial Neoplasia ◽  
Automatic Classification ◽  
Support Vector ◽  
Acid Test

Cervical cancer represents a major cause of death for women. Automatic classification of cervical images from acetic acid test could serve as a promising screening tool for cervical cancer. Despite an increasing volume of studies on automatic classification of cervical images, reported methods varied markedly in terms of features and classifiers used, and therefore the performance. The classification performance using different configurations of the classifier has not been well characterized. The objective of this study was to evaluate several frequently used features and classifiers in acetic-acid cervical image based cervical intraepithelial neoplasia classification. Seven typically used color or texture-based features and four frequently used classifiers (Support Vector Machine, Random Forest, Back-Propagation Neural Network and K-Nearest Neighbors) were included in the comparison based on a balanced large sample size including 175 CIN negative and 175 CIN positive patients. The results showed that the Support Vector Machine demonstrated the best classification accuracy when a subset of features was used. The finding of this study may provide useful reference values to the development of an automatic cervical cancer screening tool.

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