Ultrasound Classification of Breast Masses Using a Comprehensive Nakagami Imaging and Machine Learning Framework

TL;DR

This paper presents a novel framework combining Nakagami parametric imaging and machine learning to classify breast masses from ultrasound scans with high accuracy, potentially aiding in non-invasive breast cancer diagnosis.

Contribution

It introduces a comprehensive approach using Nakagami imaging features and SVM classification, optimizing window size and feature selection for improved accuracy.

Findings

Maximum classification accuracy of 93.08%

AUC of 0.9712 indicating excellent discrimination

Zero false negatives achieved in the study

Abstract

In this study we investigate the potential of parametric images formed from ultrasound B-mode scans using the Nakagami distribution for non-invasive classification of breast lesions. Through a sliding window technique, we generated seven types of parametric images from each patient scan in our dataset using basic and as well as derived parameters of the Nakagami distribution. To determine the most suitable window size for image generation, we conducted an empirical analysis using three windows, and selected the best one for our study. From the parametric images formed for each patient, we extracted a total of 72 features. Feature selection was performed to find the optimum subset of features for the best classification performance. Incorporating the selected subset of features with the Support Vector Machine (SVM) classifier, and by tuning the decision threshold, we obtained a maximum classification accuracy of 93.08%, an Area under the ROC Curve (AUC) of 0.9712, a False Negative Rate of 0%, and a very low False Positive Rate of 8.65%. Our results indicate that the high accuracy of such a procedure may assist in the diagnostic process associated with detection of breast cancer, as well as help to reduce false positive diagnosis.