Image Moment-Based Features for Mass Detection in Breast US Images via Machine Learning and Neural Network Classification Models

Differentiating between malignant and benign masses using machine learning in the recognition of breast ultrasound (BUS) images is a technique with good accuracy and precision, which helps doctors make a correct diagnosis. The method proposed in this paper integrates Hu's moments in the analysis of the breast tumor. The extracted features feed a k-nearest neighbor (k-NN) classifier and a radial basis function neural network (RBFNN) to classify breast tumors into benign and malignant. The raw images and the tumor masks provided as ground-truth images belong to the public digital BUS images database. Certain metrics such as accuracy, sensitivity, precision, and F1-score were used to evaluate the segmentation results and to select Hu's moments showing the best capacity to discriminate between malignant and benign breast tissues in BUS images. Regarding the selection of Hu's moments, the k-NN classifier reached 85% accuracy for moment M1 and 80% for moment M5 whilst RBFNN reached an accuracy of 76% for M1. The proposed method might be used to assist the clinical diagnosis of breast cancer identification by providing a good combination between segmentation and Hu's moments.

Automated summary

This study uses machine learning to differentiate malignant and benign masses in breast ultrasound images, achieving high accuracy and precision and aiding doctors in making accurate diagnoses. By integrating Hu's moments into the analysis of breast tumors, the proposed method extracts features that are fed into classifiers to classify tumors into benign and malignant. The combination of segmentation and Hu's moments shows promising potential in assisting clinical diagnosis of breast cancer.