scholarly journals Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine

PLoS ONE ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. e0211944 ◽  
Author(s):  
Jorge Arturo Zavala Bojorquez ◽  
Pierre-Marc Jodoin ◽  
Stéphanie Bricq ◽  
Paul Michael Walker ◽  
François Brunotte ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Relaxation Times ◽  
Automatic Classification ◽  
Support Vector ◽  
Pelvic Mri

scholarly journals Automatic classification of harmonic data using $k$-means and least square support vector machine

2015 ◽  
Vol 23 ◽  
pp. 1312-1325 ◽  
Author(s):  
Hüseyin ERİŞTİ ◽  
Vedat TÜMEN ◽  
Özal YILDIRIM ◽  
Belkıs ERİŞTİ ◽  
Yakup DEMİR
Keyword(s):  
Support Vector Machine ◽  
Automatic Classification ◽  
Least Square ◽  
Support Vector

Automatic classification of normal and sick patients with crackles using wavelet packet decomposition and support vector machine

2021 ◽  
Vol 67 ◽  
pp. 102521
Author(s):  
Paweł Stasiakiewicz ◽  
Andrzej P. Dobrowolski ◽  
Tomasz Targowski ◽  
Natalia Gałązka-Świderek ◽  
Teresa Sadura-Sieklucka ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wavelet Packet ◽  
Automatic Classification ◽  
Support Vector ◽  
Wavelet Packet Decomposition

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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