Machine learning methods for 1D ultrasound breast cancer screening

Neil Joshi; Seth Billings; Erika Schwartz; Susan Harvey; Philippe Burlina

doi:10.1109/ICMLA.2017.00-76

Machine learning methods for 1D ultrasound breast cancer screening

Neil Joshi, Seth Billings, Erika Schwartz, Susan Harvey, Philippe Burlina

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

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- A nd 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.

Original language	English (US)
Title of host publication	Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Editors	Xuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	711-715
Number of pages	5
ISBN (Electronic)	9781538614174
DOIs	https://doi.org/10.1109/ICMLA.2017.00-76
State	Published - 2017
Event	16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico Duration: Dec 18 2017 → Dec 21 2017

Publication series

Name	Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Volume	2017-December

Other

Other	16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Country/Territory	Mexico
City	Cancun
Period	12/18/17 → 12/21/17

Keywords

automated diagnostics
breast cancer
machine learning
ultrasound

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications

Access to Document

10.1109/ICMLA.2017.00-76

Cite this

Joshi, N., Billings, S., Schwartz, E., Harvey, S., & Burlina, P. (2017). Machine learning methods for 1D ultrasound breast cancer screening. In X. Chen, B. Luo, F. Luo, V. Palade, & M. A. Wani (Eds.), Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 (pp. 711-715). (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2017.00-76

Machine learning methods for 1D ultrasound breast cancer screening. / Joshi, Neil; Billings, Seth; Schwartz, Erika et al.
Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. ed. / Xuewen Chen; Bo Luo; Feng Luo; Vasile Palade; M. Arif Wani. Institute of Electrical and Electronics Engineers Inc., 2017. p. 711-715 (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017; Vol. 2017-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Joshi, N, Billings, S, Schwartz, E, Harvey, S & Burlina, P 2017, Machine learning methods for 1D ultrasound breast cancer screening. in X Chen, B Luo, F Luo, V Palade & MA Wani (eds), Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 711-715, 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, Cancun, Mexico, 12/18/17. https://doi.org/10.1109/ICMLA.2017.00-76

Joshi N, Billings S, Schwartz E, Harvey S, Burlina P. Machine learning methods for 1D ultrasound breast cancer screening. In Chen X, Luo B, Luo F, Palade V, Wani MA, editors, Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 711-715. (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017). doi: 10.1109/ICMLA.2017.00-76

Joshi, Neil ; Billings, Seth ; Schwartz, Erika et al. / Machine learning methods for 1D ultrasound breast cancer screening. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017. editor / Xuewen Chen ; Bo Luo ; Feng Luo ; Vasile Palade ; M. Arif Wani. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 711-715 (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017).

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abstract = "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- A nd 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.",

keywords = "automated diagnostics, breast cancer, machine learning, ultrasound",

author = "Neil Joshi and Seth Billings and Erika Schwartz and Susan Harvey and Philippe Burlina",

note = "Funding Information: *This work was supported by JHU-APL internal research and development funds Publisher Copyright: {\textcopyright} 2017 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 ; Conference date: 18-12-2017 Through 21-12-2017",

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AB - 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- A nd 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.

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Machine learning methods for 1D ultrasound breast cancer screening

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