Colorectal Polyps Detection Using Texture Features and Support Vector Machine

2008 ◽  
pp. 62-72 ◽  
Author(s):  
Da-Chuan Cheng ◽  
Wen-Chien Ting ◽  
Yung-Fu Chen ◽  
Qin Pu ◽  
Xiaoyi Jiang
Keyword(s):  
Support Vector Machine ◽  
Texture Features ◽  
Colorectal Polyps ◽  
Support Vector

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

2010 ◽  
Vol 36 (3) ◽  
pp. 1503-1510 ◽  
Author(s):  
U. Rajendra Acharya ◽  
E. Y. K. Ng ◽  
Jen-Hong Tan ◽  
S. Vinitha Sree
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Texture Features ◽  
Breast Cancer Detection ◽  
Support Vector

Automatic Fruit Disease Classification Using Images

2014 ◽  
pp. 82-100 ◽  
Author(s):  
Shiv Ram Dubey ◽  
Anand Singh Jalal
Keyword(s):  
Support Vector Machine ◽  
Apple Scab ◽  
Texture Features ◽  
Disease Classification ◽  
Economic Losses ◽  
Support Vector ◽  
Test Case ◽  
Agricultural Industry ◽  
Fruit Disease

Diseases in fruit cause devastating problems in economic losses and production in the agricultural industry worldwide. In this chapter, a method to detect and classify fruit diseases automatically is proposed and experimentally validated. The image processing-based proposed approach is composed of the following main steps: in the first step K-Means clustering technique is used for the defect segmentation, in the second step some color and texture features are extracted from the segmented defected part, and finally diseases are classified into one of the classes by using a multi-class Support Vector Machine. The authors have considered diseases of apple as a test case and evaluated the approach for three types of apple diseases, namely apple scab, apple blotch, and apple rot, along with normal apples. The experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. The classification accuracy for the proposed approach is achieved up to 93% using textural information and multi-class support vector machine.

scholarly journals Broccoli Seedling Segmentation Based on Support Vector Machine Combined With Color Texture Features

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 168565-168574
Author(s):  
Kunlin Zou ◽  
Luzhen Ge ◽  
Chunlong Zhang ◽  
Ting Yuan ◽  
Wei Li
Keyword(s):  
Support Vector Machine ◽  
Texture Features ◽  
Support Vector ◽  
Color Texture

Classification of Foot Thermograms using Texture Features and Support Vector Machine

2021 ◽  
Author(s):  
Josephine Selle J ◽  
K V M Vara Prakash ◽  
G Arun Sai ◽  
B Vinod ◽  
Kalaivani Chellappan
Keyword(s):  
Support Vector Machine ◽  
Texture Features ◽  
Support Vector

scholarly journals SUPPORT VECTOR MACHINE AND DECISION TREE BASED CLASSIFICATION OF SIDE-SCAN SONAR MOSAICS USING TEXTURAL FEATURES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 27-34 ◽  
Author(s):  
H. K. Febriawan ◽  
P. Helmholz ◽  
I. M. Parnum
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Image Classification ◽  
Texture Features ◽  
Supervised Machine Learning ◽  
Sidescan Sonar ◽  
Support Vector ◽  
Side Scan Sonar ◽  
Gaussian Kernels

<p><strong>Abstract.</strong> The diversity and heterogeneity of coastal, estuarine and stream habitats has led to them becoming a prevalent topic for study. Woody ruins are areas of potential riverbed habitat, particularly for fish. Therefore, the mapping of those areas is of interest. However, due to the limited visibility in some river systems, satellites, airborne or other camera-based systems (passive systems) cannot be used. By contrast, sidescan sonar is a popular underwater acoustic imaging system that is capable of providing high- resolution monochromatic images of the seafloor and riverbeds. Although the study of sidescan sonar imaging using supervised classification has become a prominent research subject, the use of composite texture features in machine learning classification is still limited. This study describes an investigation of the use of texture analysis and feature extraction on side-scan sonar imagery in two supervised machine learning classifications: Support Vector Machine (SVM) and Decision Tree (DT). A combination of first- order texture and second-order texture is investigated to obtain the most appropriate texture features for the image classification. SVM, using linear and Gaussian kernels along with Decision Tree classifiers, was examined using selected texture features. The results of overall accuracy and kappa coefficient revealed that SVM using a linear kernel leads to a more promising result, with 77% overall accuracy and 0.62 kappa, than SVM using either a Gaussian kernel or Decision Tree (60% and 73% overall accuracy, and 0.39 and 0.59 kappa, respectively). However, this study has demonstrated that SVM using linear and Gaussian kernels as well as a Decision Tree makes it capable of being used in side-scan sonar image classification and riverbed habitat mapping.</p>

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