Texture description and segmentation through fractal geometry
Computer Vision, Graphics, and Image Processing
Texture features based on texture spectrum
Pattern Recognition
The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise classification and support vector machines
Advances in kernel methods
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
An SVM-based Algorithm for Identification of Photosynthesis-specific Genome Features
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Fuzzy support vector machine for multi-class text categorization
Information Processing and Management: an International Journal
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Visualization and analysis of classifiers performance in multi-class medical data
Expert Systems with Applications: An International Journal
Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification
Expert Systems with Applications: An International Journal
Using Gabor filter for the illumination invariant recognition of color texture
Mathematics and Computers in Simulation
Orthogonal forward selection and backward elimination algorithms for feature subset selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines
Artificial Intelligence in Medicine
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers and Electronics in Agriculture
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
International Journal of Mobile Learning and Organisation
Hi-index | 12.06 |
This article proposes an effort to apply the multi-class support vector machine classifiers to classify the supraspinatus image into different disease groups that are normal, tendon inflammation, calcific tendonitis and supraspinatus tear. The supraspinatus tendon is often involved in the above-mentioned disease groups. Four different texture analysis methods - texture feature coding method, gray-level co-occurrence matrix, fractal dimension evaluation and texture spectrum - are used to extract features of tissue characteristic in the ultrasonic supraspinatus images. The mutual information criterion is adopted to select the powerful features from ones generated from the above-mentioned four texture analysis methods in the training stage, meanwhile, the five implementations of multi-class support vector machine classifiers are also designed to discriminate each image into one of the four disease groups in the classification stage. In experiments, the most commonly used performance measures including sensitivity, specificity, classification accuracy and false-negative rate are applied to evaluate the classification of the five implantations of multi-class support vector machines. In addition, the receiver operating characteristics analysis is also used to analyze the classification capability. The present results demonstrate that the implementation of multi-class fuzzy support vector machine can achieve 90% classification accuracy, and performance measures of this implementation are significantly superior to the others.