Classifying Breast Tumours on Ultrasound Images Using a Hybrid Classifier and Texture Features

Abstract

This work aims to classify breast tumours on ultrasound (US) images, using texture features calculated from complexity curve (CC) and grey-level co-occurence matrix (GLCM), applied to a proposed hybrid classifier based on a multilayer perceptron (MLP) network and genetic algorithms (GA). A rectangular region of interest (ROI) containing the tumour and its neighbouring is defined for each image. Five features are extracted from CC of the ROI, and another five are calculated from GLCM also for the ROI. The same is obtained for internal tumour region, hence totalling 20 parameters. The hybrid classifier uses GA to select the best set of input features, limited up to 5, while MLP is trained by the backpropagation algorithm. The leave- one-case-out re-sampling method is carried out to assure the reliability and effectiveness of the classifier. The results are compared to the ones presented in a previous work, where Fisher's Linear Discriminant Analysis (LDA) was applied. The proposed hybrid classifier achieved a global performance superior to 90.0% and statistically significant higher than LDA. Hence, our findings suggest that the combination of texture features and the hybrid classifier can aid radiologists in making the diagnoses of malignant breast tumours on US images.