Performance evaluation of multiple classification of the ultrasonic supraspinatus images by using ML, RBFNN and SVM classifiers

This paper proposed an effort to apply the several multi-class classifiers that are the maximum likelihood classifier, the radial basis function neural network, the fuzzy support vector machine and the error correcting output codes method to classify the ultrasonic supraspinatus images. The maximum mutual information criterion is adopted to search for the powerful features generating from the first order histogram statistics, gray-level co-occurrence matrix and texture feature coding method. In experiments, the most commonly used performance measures including the accuracy, sensitivity, accuracy and F_score are applied to evaluate the classification of the four classifiers. In addition, the Youden’s index, the discriminant power and the area of receiver operating characteristics curve are also used to analyze the classification capability. The experimental results demonstrate that the implementation of radial bass function neural network can achieve 94.1% classification accuracy and performance measures are significantly superior to the others.