Machine Learning Methods for 1D Ultrasound Breast Cancer Screening
| Autor: | Seth Billings, Susan Harvey, Neil Joshi, Philippe Burlina, Erika Schwartz |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Ultrasound device
business.industry Computer science Ultrasound Feature extraction 02 engineering and technology Machine learning computer.software_genre medicine.disease 030218 nuclear medicine & medical imaging Ultrasonic imaging 03 medical and health sciences 0302 clinical medicine Breast cancer Cancer screening 0202 electrical engineering electronic engineering information engineering medicine Leverage (statistics) 020201 artificial intelligence & image processing Artificial intelligence business computer Ultrasound breast |
| Zdroj: | ICMLA |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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