Cited by 358AI in cancer detectionRadiomics and Machine Learning in Medical ImagingMedical Image Segmentation Techniques

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A deep learning framework for supporting the classification of breast lesions in ultrasound images

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Han, Seokmin, Kang, Ho-Kyung, Jeong, Ja-Yeon, Park, Moon-Ho, Kim, Wonsik, Bang, Won-Chul, Seong, Yeong-Kyeong·2017·Physics in Medicine & Biology

DOI 10.1088/1361-6560/aa82ec

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Abstract

In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.

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Authors: Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K.
Affiliations: Korea National University of Transportation (KR)
Samsung Electronics (South Korea) (KR)
Corresponding author: Seong, Yeong-Kyeong
ORCID: Han, Seokmin
Park, Moon-Ho
Kim, Wonsik
Bang, Won-Chul
Seong, Yeong-Kyeong
Venue: Physics in Medicine & Biology
Publisher: IOP Publishing
Published: 2017-09-15
Volume: 62
Issue: 19
Pages: 7714-7728
ISSN: 0031-9155 (ISSN-L)
1361-6560
DOI: 10.1088/1361-6560/aa82ec
OpenAlex: W2740028789
Cited by: 358
References: 71
Citations by year: 2026: 13 · 2025: 31 · 2024: 48 · 2023: 51 · 2022: 55 · 2021: 61 · 2020: 52 · 2019: 34 · 2018: 13
FWCI: 16.14
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: publishingsupport.iopscience.iop.org/iop-standard/v1 (vor)
iopscience.iop.org/info/page/text-and-data-mining (tdm)
Locations: Physics in Medicine and Biology (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords: breast ultrasound, breast lesion classification, benign versus malignant tumors, computer-aided diagnosis, GoogLeNet, convolutional neural networks, image augmentation
MeSH terms: Machine Learning, Breast Neoplasms, Diagnosis, Computer-Assisted, Female, Humans, Image Interpretation, Computer-Assisted, Middle Aged, ROC Curve, Ultrasonography, Mammary, Neural Networks, Computer

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A deep learning framework for supporting the classification of breast lesions in ultrasound images

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