Machine Learning Methods for 1D Ultrasound Breast Cancer Screening
Neil Joshi, Seth Billings, Erika Schwartz, Susan C. Harvey, Philippe Burlina · 2017
This study addresses the development of machine learning methods for reduced space ultrasound to perform automated prescreening of breast cancer. The use of ultrasound in low-resource settings is constrained by lack of trained personnel and equipment costs, and motivates the need for automated, low-cost diagnostic tools. We hypothesize a solution to this problem is the use of 1D ultrasound (single piezoelectric element). We leverage random forest classifiers to classify 1D samples of various types of tissue phantoms simulating cancerous, benign lesions, and non-cancerous tissues. In addition, we investigate the optimal ultrasound power and frequency parameters to maximize performance. We show preliminary results on 2-, 3- and 5-class classification problems for the ideal power/frequency combination. These results demonstrate promise towards the use of a single-element ultrasound device to screen for breast cancer.