Improving the diagnosis of breast cancer based on biophysical ultrasound
features utilizing machine learning
- URL: http://arxiv.org/abs/2207.06560v1
- Date: Wed, 13 Jul 2022 23:53:09 GMT
- Title: Improving the diagnosis of breast cancer based on biophysical ultrasound
features utilizing machine learning
- Authors: Jihye Baek, Avice M. O'Connell, Kevin J. Parker
- Abstract summary: We propose a biophysical feature based machine learning method for breast cancer detection.
The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The improved diagnostic accuracy of ultrasound breast examinations remains an
important goal. In this study, we propose a biophysical feature based machine
learning method for breast cancer detection to improve the performance beyond a
benchmark deep learning algorithm and to furthermore provide a color overlay
visual map of the probability of malignancy within a lesion. This overall
framework is termed disease specific imaging. Previously, 150 breast lesions
were segmented and classified utilizing a modified fully convolutional network
and a modified GoogLeNet, respectively. In this study multiparametric analysis
was performed within the contoured lesions. Features were extracted from
ultrasound radiofrequency, envelope, and log compressed data based on
biophysical and morphological models. The support vector machine with a
Gaussian kernel constructed a nonlinear hyperplane, and we calculated the
distance between the hyperplane and data point of each feature in
multiparametric space. The distance can quantitatively assess a lesion, and
suggest the probability of malignancy that is color coded and overlaid onto B
mode images. Training and evaluation were performed on in vivo patient data.
The overall accuracy for the most common types and sizes of breast lesions in
our study exceeded 98.0% for classification and 0.98 for an area under the
receiver operating characteristic curve, which is more precise than the
performance of radiologists and a deep learning system. Further, the
correlation between the probability and BI RADS enables a quantitative
guideline to predict breast cancer. Therefore, we anticipate that the proposed
framework can help radiologists achieve more accurate and convenient breast
cancer classification and detection.
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