The DOI matches its Crossref record; 45 of the 47 references it cites were found in a registry or an index.
Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks
Open accessAbstract
Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.
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Metadata
- Authors
- Yap, M. H., Pons, G., Martí, J., Ganau, S., Sentís, M., Zwiggelaar, R., Davison, A. K., & Martí, R.
- Affiliations
- Manchester Metropolitan University (GB)
- Universitat Oberta de Catalunya (ES)
- Department of Computer Science, University of Girona, Girona, Spain
- Corporació Sanitària Parc Taulí (ES)
- Radiology Department, UDIAT-Centre Diagnòstic, Corporació Parc Taulí, Sabadell, Spain
- Department of Computer Science, Aberystwyth University, Aberystwyth, U.K.
- University of Manchester (GB)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2018-07
- Volume
- 22
- Issue
- 4
- Pages
- 1218-1226
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2744692634
- Cited by
- 1,086
- References
- 47
- Citations by year
- 2026: 133 · 2025: 167 · 2024: 159 · 2023: 159 · 2022: 149 · 2021: 134 · 2020: 97 · 2019: 63 · 2018: 24
- FWCI
- 29.76
- Type
- journal article
- Language
- en
- Open access
- green
- e-space.mmu.ac.uk
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- The Oberta in Open Access (Universitat Oberta de Catalunya) (publishedVersion)
- e-space (Manchester Metropolitan University) (acceptedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion, other-oa)
- Funders
- Spanish Ministry of Economy and Competitiveness under Project SMARTER ((DPI2015-68442-R))
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast ultrasound, lesion detection, computer-aided diagnosis, convolutional neural networks, U-Net, LeNet, transfer learning, breast cancer, benign versus malignant differentiation, benchmark dataset, deformable part models
- MeSH terms
- Algorithms, Breast, Breast Neoplasms, Female, Humans, Image Interpretation, Computer-Assisted, Databases, Factual, Ultrasonography, Mammary, Neural Networks, Computer
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