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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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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
(2010) · Cited by 8,261
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Powers, D. M. W. (2020) · arXiv · doi:10.48550/arxiv.2010.16061 · Cited by 5,440
Convolutional networks for images, speech, and time series
LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,646
The Handbook of brain theory and neural networks
(1996) · Choice Reviews Online · doi:10.5860/choice.33-4484 · Cited by 3,913
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
Measurement Reliability and Validity
(2000) · Research Methods in Applied Settings · doi:10.4324/9781410605337-29 · Cited by 2,541
Speckle reducing anisotropic diffusion
Yu, Y., & Acton, S. T. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.804276 · Cited by 1,854
Deep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,789
Solid breast nodules: use of sonography to distinguish between benign and malignant lesions.
Stavros, A. T., Thickman, D., Rapp, C., Dennis, M. A., Parker, S. H., & Sisney, G. A. (1995) · Radiology · doi:10.1148/radiology.196.1.7784555 · Cited by 1,771
(2012) · neural information processing systems · Cited by 1,291
(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229
Large scale deep learning for computer aided detection of mammographic lesions
Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965
Pedestrian Detection with Unsupervised Multi-stage Feature Learning
Sermanet, P., Kavukcuoglu, K., Chintala, S., & LeCun, Y. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.465 · Cited by 762
Automated breast cancer detection and classification using ultrasound images: A survey
Cheng, H.-D., Shan, J., Ju, W., Guo, Y., & Zhang, L. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.05.012 · Cited by 729
Handbook of computer vision and applications
Jähne, B., Haussecker, H. W., & Geißler, P. (1999) · Cited by 658
Toward automatic phenotyping of developing embryos from videos
Ning, F., Delhomme, D., LeCun, Y., Piano, F., Bottou, L., & Barbano, P. E. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.852470 · Cited by 267
Computerized lesion detection on breast ultrasound
Drukker, K., Giger, M. L., Horsch, K., Kupinski, M. A., Vyborny, C. J., & Mendelson, E. B. (2002) · Medical Physics · doi:10.1118/1.1485995 · Cited by 234
Understanding the Mechanisms of Deep Transfer Learning for Medical Images
Ravishankar, H., Sudhakar, P., Venkataramani, R., Thiruvenkadam, S. R., Annangi, P., Babu, N., & Vaidya, V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_20 · Cited by 203
Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms
Dhungel, N., Carneiro, G., & Bradley, A. P. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_74 · Cited by 162
Shan, J., Cheng, H.-D., & Wang, Y. (2012) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2011.10.022 · Cited by 159
A novel algorithm for initial lesion detection in ultrasound breast images
Yap, M. H., Edirisinghe, E. A., & Bez, H. (2008) · Journal of Applied Clinical Medical Physics · doi:10.1120/jacmp.v9i4.2741 · Cited by 100
Self-citation3-D breast ultrasound segmentation using active contour model
Chen, D., Chang, R., Wu, W.-J., Moon, W. K., & Wu, W.-L. (2003) · Ultrasound in Medicine & Biology · doi:10.1016/s0301-5629(03)00059-0 · Cited by 99
Huynh, B. Q., Drukker, K., & Giger, M. L. (2016) · Medical Physics · doi:10.1118/1.4957255 · Cited by 91
Akın, O., Brennan, S. B., Dershaw, D. D., Ginsberg, M. S., Gollub, M. J., Schöder, H. M., Panicek, D. M., & Hricak, H. (2012) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21156 · Cited by 71
Mordang, J., Janssen, T., Bria, A., Kooi, T., Gubern‐Mérida, A., & Karssemeijer, N. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-41546-8_5 · Cited by 65
Drukker, K., Gruszauskas, N. P., Sennett, C. A., & Giger, M. L. (2008) · Radiology · doi:10.1148/radiol.2482071778 · Cited by 52
Ahn, C., Heo, C., Jin, H., & Kim, J. H. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2254264 · Cited by 41
Processed images in human perception: A case study in ultrasound breast imaging
Yap, M. H., Edirisinghe, E. A., & Bez, H. (2009) · European Journal of Radiology · doi:10.1016/j.ejrad.2008.11.007 · Cited by 34
Self-citationDemirkaya, O., Asyali, M. H., & Sahoo, P. K. (2008) · CRC Press eBooks · doi:10.1201/9781420008937-9 · Cited by 34
Object Boundary Detection in Ultrasound Images
Yap, M. H., Edirisinghe, E. A., & Bez, H. (2006) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2006.51 · Cited by 19
Self-citationA multifractal approach for auto-segmentation of SAR images
Gan, D., & Tat-Soon, Y. (2002) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2001.977982 · Cited by 17
Computerized Detection of Breast Lesions Using Deformable Part Models in Ultrasound Images
Pons, G., Martí, R., Ganau, S., Melcior Sentis, C., & Martı́, J. (2014) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2014.03.005 · Cited by 14
Self-citationFeasibility Study of Lesion Detection Using Deformable Part Models in Breast Ultrasound Images
Pons, G., Martí, R., Ganau, S., Melcior Sentis, C., & Martı́, J. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38628-2_32 · Cited by 13
Self-citation
Research works printed but not linked: 2
- Kupinski, 1999, Computerized detection of mass lesions in digital mammography using radial gradient index filtering, Radiology, 213, 229
- Prapavesis, 2003, Breast Ultrasound and US-Guided Interventional Techniques: A Multimedia Teaching File
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Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., et al. (2019) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21552 · Cited by 1,846
Transformers in medical imaging: A survey
Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102802 · Cited by 1,523
A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation
Abraham, N., & Khan, N. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759329 · Cited by 982
Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798
Yeung, M., Sala, E., Schönlieb, C., & Rundo, L. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.102026 · Cited by 795
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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