Cited by 1,086AI in cancer detectionMedical Image Segmentation TechniquesRadiomics and Machine Learning in Medical Imaging

Automated breast ultrasound lesions detection using convolutional neural networks

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Yap, Moi Hoon, Pons, Gerard, Martí, Joan, Ganau, Sergi, Sentís, Melcior, Zwiggelaar, Reyer, Davison, Adrian K., Martí, Robert·2018·IEEE Journal of Biomedical and Health Informatics

Abstract

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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Crossref · 10.1109/jbhi.2017.2731873

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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 · 2017: 1
FWCI
29.76
Type
journal article
Language
en
Open access
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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