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A deep learning framework for supporting the classification of breast lesions in ultrasound images
Open accessAbstract
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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“In addition, the stacked denoising auto-encoder (SDAE) [88], a combination of the point-wise gated Boltzmann machine (PGBM) and RBM [89], and the GoogLeNet CNN [90] have been applied to breast US or shear-wave elastography images for breast cancer diagnosis; all of these obtained a superior performance when compared with human experts.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [90]
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Metadata
- 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
- 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
- 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
- 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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