Research on pattern classification methods for ultrasound breast tumor image

Objective To develop a computer-aided diagnosis with multiple features based on Pattern classification methods to differentiate benign from malignant breast tumor. Methods In this paper, optimal feature vector was firstly obtained from features extracted from Ultrasound Breast Tumor Image using Sequential Forward Selection Algorithm, then four pattern classification methods was used to classify the breast tumor, these pattern classification methods include SVM, BP, Bayes and Fisher classifier. Results Experiments on 200 ultrasonic images, randomly divided into training set 100 and prediction set 100, showed that the Accuracy of SVM, Bayes, BP and Fisher was 0.960, 0.940, 0.932±0.013, 0.930 respectively. Conclusion SVM classifier has the best performance and can effectively differentiate benign and malignant lesions.