Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images

Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images

Abstract Background The purpose of this work was to evaluate computable Breast Imaging Reporting and Data System (BI-RADS) radiomic features to classify breast masses on ultrasound B-mode images. Methods The database consisted of 206 consecutive lesions (144 benign and 62 malignant) proved by percut...

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Bibliographic Details
Main Authors: Eduardo Fleury, Karem Marcomini
Format: Article
Language:English
Published: SpringerOpen 2019-08-01
Series:European Radiology Experimental
Subjects:
Breast neoplasms
Machine Learning
Neural networks (computer)
Support vector machine
Ultrasonography
Online Access:http://link.springer.com/article/10.1186/s41747-019-0112-7

Internet

http://link.springer.com/article/10.1186/s41747-019-0112-7

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