Classification of Liver Tumor using SFTA based Naive Bayes Classifier and Support Vector Machine

被引：0

作者：

Krishna, Anju M. ^{[1
]}

Edwin, Deepesh ^{[1
]}

Hariharan, S. ^{[2
]}

机构：

[1] LBSITW, Dept ECE, Trivandrum, Kerala, India

[2] Coll Engn, Dept EEE, Trivandrum, Kerala, India

来源：

2017 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, INSTRUMENTATION AND CONTROL TECHNOLOGIES (ICICICT) | 2017年

关键词：

CT image; feature extraction; SFTA algorithm; tumor Segmentation; Naive Bays Classifier; Support vector machine; CT IMAGES;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Liver cancer is one of the powerful threats faced by the society and its detection at its early stage will reduce the mortality rate drastically. Different non-invasive imaging techniques are used for this purpose. In this work CT imaging techniques are used. This produce good quality images and they are cheaper compared to MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography). Here SFTA (Segmentation based Fractal Texture Analysis) method is used for feature extraction and Naive Bayes classifier and Support vector machine are for classification. The performance of two classifiers was compared and result show that SVM classifier gives better classification accuracy of 92.5% over Wye Bayes Classifier.

引用

页码：1066 / 1070

页数：5

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