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

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 access

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

DOI 10.1109/jbhi.2017.2731873

Open this paper in CoChat — free account Open PDF

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.

Integrity signals

Registry

Crossref · 10.1109/jbhi.2017.2731873

Cites

45 of 45 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

The text we found for this paper looks like another document, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The text we found for this paper looks like another document, so the self-check wasn’t run.

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
Deep learning
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

Show all 47 references

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

Sorted by citations

Items 601–700 of 1,000

Distance guided generative adversarial network for explainable medical image classifications
Xiong, X., Sun, Y., Liu, X., Ke, W., Lam, C., Chen, J., Jiang, M., Wang, M., Xie, H., Tong, T., et al. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102444 · Cited by 6
Syn-Net: A Synchronous Frequency-Perception Fusion Network for Breast Tumor Segmentation in Ultrasound Images
Zhao, G., Zhu, X., Wang, X., Yan, F., & Guo, M. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3514134 · Cited by 6
CMAUNeXt: An Efficient Neural Network Based on Large Kernel and Multi-Dimensional Attention Module for Breast Tumor Segmentation
Prasetyo, H., Ashidiqy, R. B., & Salamah, U. (2024) · International Conference on Smart Mechatronics (ICSMech) · doi:10.1109/icsmech62936.2024.10812276 · Cited by 6
DC-Net: Decomposing and coupling saliency map for lesion segmentation in ultrasound images
Ning, Z., Mao, Y., Xu, X., Feng, Q., Zhong, S., & Zhang, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110355 · Cited by 6
Automated Breast Lesion Segmentation from Ultrasound Images based on PPU-Net
Zhao, Y., Que, D., Tan, J., Xiao, Y., & Yu, Y. (2019) · IEEE International Conference on Medical Imaging Physics and Engineering (ICMIPE) · doi:10.1109/icmipe47306.2019.9098209 · Cited by 6
An Early Breast Cancer Detection System Using Recurrent Neural Network (RNN) with Animal Migration Optimization (AMO) Based Classification Method
Prakash, S., & Sangeetha, K. (2021) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2021.3885 · Cited by 6
ECU-Net: multi-scale salient boundary detection and contrast feature enhancement U-Net for breast ultrasound image segmentation
Han, C., Huang, X., Zhang, Y., & Wang, M. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02445-3 · Cited by 6
US-Net: A Breast Ultrasound Image Segmentation using Deep Learning
Erragzi, N., Zrira, N., Jimi, A., Benmiloud, I., Sebihi, R., & Ngote, N. (2023) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3625007.3627304 · Cited by 6
LightBTSeg: A Lightweight Breast Tumor Segmentation Model Using Ultrasound Images via Dual-Path Joint Knowledge Distillation
Guo, H., Wang, S., Dang, H., Xiao, K., Yang, Y., Liu, W., Liu, T., & Wan, Y. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10450290 · Cited by 6
Breast Cancer Detection Using Deep Belief Network by Applying Feature Extraction on Various Classifiers
Et. al. Prasath Alias Surendhar, S. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i1s.1909 · Cited by 6
Ultrasound Image Features under Deep Learning in Breast Conservation Surgery for Breast Cancer
Zhang, H., Liu, H., Ma, L., Liu, J., & Hu, D. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/6318936 · Cited by 6
Weakly-Supervised Ultrasound Video Segmentation with Minimal Annotations
Chang, R., Wang, D., Guo, H., Ding, J., & Wang, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_62 · Cited by 6
A gated convolutional neural network for classification of breast lesions in ultrasound images
Feizi, A. (2022) · Soft Computing · doi:10.1007/s00500-022-07024-9 · Cited by 6
An Ultra-Fast Method for Simulation of Realistic Ultrasound Images
Sharifzadeh, M., Benali, H., & Rivaz, H. J. (2021) · IEEE International Ultrasonics Symposium · doi:10.1109/ius52206.2021.9593470 · Cited by 6
Breast Cancer Diagnosis Using Iterative Fourier-Bessel Decomposition Method Based CNN-kernel FeaturesPreprint
Chaudhary, P. K., Das, K., & Pachori, R. B. (2022) · doi:10.36227/techrxiv.21717608 · Cited by 6
Trustworthy Multi-Modal Ultrasound Fusion via Uncertainty Calibration and Conflict Resolution
Wan, P., Wei, L., Zhang, S., Xue, H., Shao, W., Kong, W., & Zhang, D. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3615589 · Cited by 5
A multi-task learning network with boundary regression for automatic ultrasound image segmentation
Huang, Z., Yang, Q., & Gao, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108828 · Cited by 5
Self-supervised learning for breast cancer detection: A review
Figueiras, H., Domingues, J., Matela, N., & Garcia, N. C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111245 · Cited by 5
A Pregnancy Prediction System based on Uterine Peristalsis from Ultrasonic Images
Mori, K., Kitaya, K., Ishikawa, T., & Hata, Y. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2021.01010 · Cited by 5
Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging
Ajmani, P., Sharma, V., Rai, R. H., & Kalra, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13999-4.00002-x · Cited by 5
A two-stage progressive deep segmentation network for tumor detection in breast ultrasound images
Zaidkilani, N., Abdel‐Nasser, M., García, M. Á., & Puig, D. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20465-8 · Cited by 5
An automatic B-snake model based on deep learning for medical image segmentation
Sefti, R., Sbibih, D., & Jennane, R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126481 · Cited by 5
An improved framework for breast ultrasound image segmentation with multiple branches depth perception and layer compression residual module
Cui, K., Tian, Q., Wang, H., & Ma, C. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110265 · Cited by 5
Breast tumor diagnosis via multimodal deep learning using ultrasound B-mode and Nakagami images
Muhtadi, S., & Gallippi, C. M. (2025) · Journal of medical imaging · doi:10.1117/1.jmi.12.s2.s22009 · Cited by 5
C5-net: Cross-organ cross-modality cswin-transformer coupled convolutional network for dual task transfer learning in lymph node segmentation and classification
Wang, M., Chen, H., Mao, L., Jiao, W., Han, H., & Zhang, Q. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102633 · Cited by 5
A self-supervised framework for improved generalisability in ultrasound B-mode image segmentation
Ellis, E., Bulpitt, A. J., Parsa, N., Byrne, M. F., & Ali, S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109089 · Cited by 5
A foundation generative model for breast ultrasound image analysis
Yu, H., Li, Y., Zhang, N., Niu, Z., Gong, X., Luo, Y., Ye, H., He, S., Wu, Q., Qin, W., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01639-1 · Cited by 10
NGMMs: Neutrosophic Gaussian Mixture Models for Breast Ultrasound Image Classification
Huang, K., Xu, M., & Qi, X. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630448 · Cited by 5
Extended Speckle Reduction Anisotropic Diffusion Filter to Despeckle Ultrasound Images
Raj, P. L. J., Krishnan, K., Gauni, S., & Manimegalai, C. T. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.026052 · Cited by 5
BUS-Net: Breast Tumour Detection Network for Ultrasound Images Using Bi-directional ConvLSTM and Dense Residual Connections
Arora, R., & Raman, B. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00733-5 · Cited by 5
Breast Ultrasound Tumor Detection Based on Active Learning and Deep Learning
Liu, G., Tan, J., Yang, H., Li, Y., Sun, X. H., Wu, J., & Luo, B. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-7946-0_1 · Cited by 5
Difficulty-aware prior-guided hierarchical network for adaptive segmentation of breast tumors
Hussain, S., Xi, X., Ullah, I., Naim, S. W., Shaheed, K., Tian, C., & Yin, Y. (2023) · Science China Information Sciences · doi:10.1007/s11432-021-3340-y · Cited by 5
A Convolutional Neural Network Based Classification Approach for Breast Cancer Detection
Rashid, M. H. O., Shahriyar, S. M., Shamrat, F. M. J. M., Shitab, T. M., Tasnim, Z., & Ahmed, M. Z. (2023) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei56765.2023.10125783 · Cited by 5
A Deep Learning Based Methodological Analysis for Breast Cancer Classification
Rajendran, T., Divya, G., Sridhar, S., & Anitha, T. (2023) · International Journal of Electronics and Communication Engineering · doi:10.14445/23488549/ijece-v10i6p106 · Cited by 5
BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization
Zafar, A., Tanveer, J., Ali, M. U., & Lee, S. W. (2023) · Bioengineering · doi:10.3390/bioengineering10070825 · Cited by 5
Classification Task Assisted Segmentation Network for Breast Tumor Segmentation in Ultrasound Images
Zhang, K., & Wang, B. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222770 · Cited by 5
Dense Swin Transformer for Classification of Thyroid Nodules
Baima, N., Wang, T., Zhao, C.-K., Chen, S., Zhao, C., & Lei, B. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340827 · Cited by 5
BCUIS‐Net : A breast cancer ultrasound image segmentation network via boundary‐aware and shape feature fusion
Li, H., Wang, X., Tang, Y., & Ye, S. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.23011 · Cited by 5
Medical image analysis of masses in mammography using deep learning model for early diagnosis of cancer tissues
Julia, R., Prince, S., & Bini, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13999-4.00006-7 · Cited by 5
Multilevel perception boundary‐guided network for breast lesion segmentation in ultrasound images
Yang, X., Zhang, J., Ou, Y., Chen, Q., Wang, L., & Wang, L. (2025) · Medical Physics · doi:10.1002/mp.17647 · Cited by 5
Innovative Ultrasound Image Denoising Using Channel Attention and Variational Autoencoders
Slimi, T., Baoues, E. B., & Khalifa, A. B. (2025) · Critical Reviews in Biomedical Engineering · doi:10.1615/critrevbiomedeng.2025058842 · Cited by 5
HMA-Net: a hybrid mixer framework with multihead attention for breast ultrasound image segmentation
Koshy, S. S., & Anbarasi, L. J. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1572433 · Cited by 5
Literature Review on Breast Cancer Diagnosis using 3D Images: Methods and Performance Analysis
Umamaheswari, T., & Babu, Y. M. M. (2021) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec51865.2021.9591861 · Cited by 5
Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures
Srikanth, V., & Krithiga, S. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.023474 · Cited by 5
Deep meta-learning for the selection of accurate ultrasound based breast mass classifier
Byra, M., Karwat, P., Ryzhankow, I., Komorowski, P., Klimonda, Z., Fura, Ł., Pawłowska, A., Żołek, N., & Litniewski, J. (2022) · IEEE International Ultrasonics Symposium · doi:10.1109/ius54386.2022.9957191 · Cited by 5
Gray-to-color image conversion in the classification of breast lesions on ultrasound using pre-trained deep neural networks
Gómez‐Flores, W., & Pereira, W. C. D. A. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02928-6 · Cited by 5
Deep learning for ultrasound medical images: artificial life variant
Karunanayake, N., & Makhanov, S. S. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09910-9 · Cited by 5
A Two-Stage Fully Automatic Segmentation Scheme Using Both 2D and 3D U-Net for Multi-sequence Cardiac MR
Xu, H., Xu, Z., Gu, W., & Zhang, Q. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-39074-7_33 · Cited by 5
Identifying diagnosis evidence of cardiogenic stroke from Chinese echocardiograph reports
Lu, Q., Xu, X., Ding, L., Li, Z., & Li, J. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1106-3 · Cited by 5
Effective diagnostic model construction based on discriminative breast ultrasound image regions using deep feature extraction
Yu, H., Sun, H., Li, J., Shi, L., Bao, N., Li, H., Qian, W., & Zhou, S. (2021) · Medical Physics · doi:10.1002/mp.14832 · Cited by 5
Semantic Segmentation of Breast Ultrasound Image with Pyramid Fuzzy Uncertainty Reduction and Direction Connectedness Feature
Huang, K., Zhang, Y., Cheng, H.-D., Xing, P., & Zhang, B. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413082 · Cited by 5
Breast intervention surgery robot under image navigation: A review
Zhang, X., Du, H., Lu, M., & Zhang, Y. (2021) · Advances in Mechanical Engineering · doi:10.1177/16878140211028113 · Cited by 5
Hybrid UNET Model Segmentation for an Early Breast Cancer Detection using Ulrasound ImagesPreprint
Ahmed, I. B. (2022) · doi:10.36227/techrxiv.19704895.v1 · Cited by 5
An annotated heterogeneous ultrasound database
Yang, Y., Chen, Y., Dong, X., Zhang, J., Long, C., Jin, Z., & Dai, Y. (2025) · Scientific Data · doi:10.1038/s41597-025-04464-4 · Cited by 5
Dendritic Kernel Convolutional Neural Network for Breast Ultrasound Images Segmentation
Zhang, H., Liu, Z., Zhang, Z., Lei, Z., Wang, Z., Hasegawa, H., & Gao, S. (2025) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3615627 · Cited by 5
EAMSHA-UNet: A breast cancer ultrasound image segmentation model based on edge-aware multi-scale hybrid attention
Ma, L., Lin, X., Liang, W., & Liu, W. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110160 · Cited by 4
Identification of women with high grade histopathology results after conisation by artificial neural networks
Mlinarič, M., Križmarić, M., Takač, I., & Fokter, A. R. (2022) · Radiology and Oncology · doi:10.2478/raon-2022-0023 · Cited by 5
Deep Learning CNNs for Breast Cancer Classification and Detection ”Enhancing Diagnostic Accuracy in Medical Practice
Hamid, M. A., Mondher, H. M., & Ayoub, B. (2024) · International Conference on Electrical Engineering and Automatic Control (ICEEAC) · doi:10.1109/iceeac61226.2024.10576560 · Cited by 4
Gradually Vanishing Bridge Based on Multi-Kernel Maximum Mean Discrepancy for Breast Ultrasound Image Classification
Xu, B., Tan, C., Wu, Y. T., & Li, F. (2024) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2024.p0835 · Cited by 4
A feature fusion method based on radiomic features and revised deep features for improving tumor prediction in ultrasound images
Wang, X., Lv, L., Tang, Q., Wang, G., Shang, E., Zheng, H., & Zhang, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109605 · Cited by 4
MHS U-Net: Multi-scale hybrid subtraction network for medical image segmentation
Qian, J., Li, H., Liao, S., Xiao, Z., Li, W., & Li, H. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110431 · Cited by 4
Effect of the image enhancement methods on ultrasound image registration
Aydın, S. G., Bılge, H. Ş., & Hardalaç, F. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.105530 · Cited by 4
Recognition of breast cancer from heterogeneous ultrasound images: A multi-level deep learning approach
Duong, L. T., Thu, D. T. H., Bui, A. M. T., & Nguyen, P. T. (2025) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2025.101698 · Cited by 4
Breast Ultrasound Image Segmentation Integrating Mamba-CNN and Feature Interaction
Yang, G., Zhang, Y., & Yang, H. (2025) · Sensors · doi:10.3390/s26010105 · Cited by 4
Transformers meet CNNs for insights into breast mass classification from histopathological images
Anand, V., & Khajuria, A. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1770667 · Cited by 4
Wavelet-CNN Feature Fusion Architecture for Robust Breast Cancer Classification in Histopathological Imaging
Bohra, M., Singh, K. U., Kumar, I., & Shah, M. A. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-026-01258-3 · Cited by 4
A deep learning system for non-invasive breast cancer diagnosis with multimodal data
Li, Y., Zhang, J., Chen, H., Yang, L., Xie, Y., Xu, Q., Li, P., Li, J., Li, Z., Dai, L., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01654-2 · Cited by 4
Combination Ultrasound and Mammography for Breast Cancer Classification using Deep Learning
Chunhapran, O., & Yampaka, T. (2021) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse53117.2021.9493840 · Cited by 4
Shift-Invariant Segmentation in Breast Ultrasound Images
Sharifzadeh, M., Benali, H., & Rivaz, H. J. (2021) · IEEE International Ultrasonics Symposium · doi:10.1109/ius52206.2021.9593568 · Cited by 4
Medical Image Classification Using DL-based Feature Extraction in IoMT
Thimmapuram, M., Salvadi, S. R., Narsimhulu, P., & Aluvalu, R. (2023) · Recent Patents on Engineering · doi:10.2174/1872212117666230222093128 · Cited by 4
Current Status, Prospect and Bottleneck of Ultrasound AI Development: A Systemic Review
Xun, M. S., Ke, P. W., Jiang, M. M., Chen, B. H., Chen, B. H., Lam, P. C., Cui, M. L., & Tan, P. T. (2023) · Advanced ultrasound in diagnosis and therapy · doi:10.37015/audt.2023.230020 · Cited by 4
TS-GCN: A novel tumor segmentation method integrating transformer and GCN
Song, H., Liu, C., Li, S., & Zhang, P. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023807 · Cited by 4
Mining Negative Temporal Contexts for False Positive Suppression in Real-Time Ultrasound Lesion Detection
Yu, H., Li, Y., Wu, Q., Zhao, Z., Chen, D., Wang, D., & Wang, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43987-2_1 · Cited by 4
CNN based breast cancer detection using artifical intelligence
Velayutham, R., & Suresh, L. P. (2023) · AIP conference proceedings · doi:10.1063/5.0171483 · Cited by 4
Investigation on ultrasound images for detection of fetal congenital heart defects
Satish, S., & Rufus, N. H. A. (2024) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ad4f91 · Cited by 4
A Breast Ultrasound Tumor Detection Framework Using Convolutional Neural Networks
Yang, H., Wang, X., Tan, J., Liu, G., Sun, X. H., & Li, Y. (2022) · International Conference on Bioinformatics and Intelligent Computing (BIC) · doi:10.1145/3523286.3524518 · Cited by 4
Breast cancer invasive carcinoma detection using deep convolution neural networks
Ramya, N., Sree, M. S., Srija, R., Kalpana, R. A., Devi, M. K., & Meera, S. (2022) · AIP conference proceedings · doi:10.1063/5.0082646 · Cited by 4
Machine Learning Algorithms for Binary Classification of Breast Cancer
katiyar, P., & Singh, K. P. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1312-1_3 · Cited by 4
Breast Ultrasound Computer-Aided Diagnosis Using Structure-Aware Triplet Path Networks
Zhang, E., Chen, W., Xu, X., Zhang, Z., & Li, J. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10445975 · Cited by 4
A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound Semantic Segmentation
Lucke, K., Vakanski, A., & Xian, M. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635867 · Cited by 4
Retracted: Breast Malignant Detection using Deep Learning Model
Timmana, H. K., & Rajabhushanam, C. (2020) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec49089.2020.9215382 · Cited by 4
Retracted
Classification of benign and malignant breast masses using entropy from nonlinear ultrasound radiofrequency signal
Zhang, M., Gao, F., Tu, J., Wu, Y., & Zhang, D. (2020) · Acta Physica Sinica · doi:10.7498/aps.70.20201919 · Cited by 4
Automatic detection of ultrasound breast lesions: a novel saliency detection model based on multiple priors
Fang, H., Cai, N., Jingwen, Z., Bai, Y., Li, J., & Wang, H. (2021) · Signal Image and Video Processing · doi:10.1007/s11760-021-02012-2 · Cited by 4
A Novel Deep Learning System for Breast Lesion Risk Stratification in Ultrasound Images
Liu, T., An, X., Liu, Y., Liu, Y., Lin, B., Jiang, R., Xu, W., Cong, L., & Zhu, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_45 · Cited by 4
Breast mass classification in ultrasound based on Kendall's shape manifoldPreprint
Byra, M., & André, M. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.11159 · Cited by 4
Edge-Aware Diffusion Segmentation Model With Hessian Priors for Automated Diaphragm Thickness Measurement in Ultrasound Imaging
Miao, C., He, Y., Shi, B., Bian, Z., Yu, W., Chen, Y., & Zhou, G. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3601567 · Cited by 3
The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation
Akinniyi, O., Dixon, J., Aina, J., Weaks, F., Saleh, G. A., Rahman, M. M., Meeker, T. J., Trivedi, H., Gichoya, J. W., & Khalifa, F. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109036 · Cited by 3
Novel Clinical Hybrid Deep Framework for Denoising and Anatomical Segmentation in Challenging Ultrasound Conditions
Slimi, T., & Khalifa, A. B. (2025) · Ultrasonic Imaging · doi:10.1177/01617346251384596 · Cited by 3
On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models
Alsharid, M., Guo, X., Men, Q., Saha, P., Mishra, D., Ahuja, R., Ouyang, C., & Noble, J. A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02162-4 · Cited by 3
Deep Learning for Tumor Segmentation and Multiclass Classification in Breast Ultrasound Images Using Pretrained Models
Kumar, K. E. A., Wilson, M. E., Blake, N. E., Yost, T. J., & Walker, M. (2025) · Sensors · doi:10.3390/s25247557 · Cited by 3
DFMFI: ultrasound breast cancer detection method based on dynamic fusion multi-scale feature interaction model
Ma, C., Zhang, H., & Guo, L. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102710 · Cited by 3
A novel approach for breast tumor segmentation using multi-resolution analysis through wavelet transform
Jasrotia, H., Singh, C., & Kaur, S. (2026) · Soft Computing · doi:10.1007/s00500-025-11060-6 · Cited by 3
CS-Net: combined ConvNeXt-Swin-Unet for accurate medical image segmentation
Tagnamas, J., Ramadan, H., Yahyaouy, A., & Tairi, H. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08453-1 · Cited by 3
Deep Learning for Medical Ultrasound Image Segmentation: A Systematic Review of the Current Research
Rainio, O., Roshan, E., Hosseini, S. M., Rehman, R., Okenwa, J., & Klén, R. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-01931-1 · Cited by 3
Wavelet Spectrum in a Multi‐Channel Network May Reduce Biopsy Rates on Diagnosis of Breast Tumors With BI ‐ RADS Category 4a or Higher
Li, Y., Han, J., Qi, S., Wang, Q., Sun, P., Xu, L., & Ma, J. (2026) · Journal of Ultrasound in Medicine · doi:10.1002/jum.70315 · Cited by 3
Exploiting Multi-Decision and Deep Refinement for Ultrasound Image Segmentation
Liu, W., Li, X., Hu, K., & Gao, X. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096071 · Cited by 3
ATFF: Advanced Transformer with Multiscale Contextual Fusion for Medical Image Segmentation
Guo, X., Wang, L., Huang, Z., Zhang, Y., & Han, Y. (2024) · Journal of Computer and Communications · doi:10.4236/jcc.2024.123015 · Cited by 3
DCFNet: An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation
Zhu, C., Zhang, R., Xiao, Y., Zou, B., Chai, X., Yang, Z., Hu, R. J., & Duan, X. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.048453 · Cited by 3
ACL-DUNet: A tumor segmentation method based on multiple attention and densely connected breast ultrasound images
Zhang, H., He, L. J., Wenjia, G., Ma, J., Gang, S., & Ma, H. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307916 · Cited by 3
Enhanced medical image segmentation via deep dynamic self-adjusting U-Net with multi-scale attention and semantic mitigation
Ou, Y., Chen, Y., Zhang, Y., Yang, S., Zhang, X., Zhou, Y., Chen, S., & Peng, L. (2025) · The Visual Computer · doi:10.1007/s00371-025-03874-0 · Cited by 3

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)
ORCID: Yap, Moi Hoon
Martí, Joan
Ganau, Sergi
Davison, Adrian K.
Venue: IEEE Journal of Biomedical and Health Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2018-07
Volume: 22
Issue: 4
Pages: 1218-1226
ISSN: 2168-2194 (ISSN-L)
2168-2208
DOI: 10.1109/jbhi.2017.2731873
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
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks