Abstract
Segmentation and classification of breast tumors in ultrasound (US) images is an important application to deep neural networks. Unet architecture is recently developed for image segmentation. However, the standard convolution used in the Unet architecture can lead to problems such as loss of information, capturing irrelevant features, and failing to accurately localize the boundary of the segmented parts in an image. Additionally, the model involves a large number of parameters, leading to high computational time. This work proposes a novel deep-learning method called EfficientU-Net for breast tumor segmentation in ultrasound (US) images. The method addresses the above challenges by integrating a modified EfficientNet with an atrous convolution (AC) block in the encoder part of the Unet model. The EfficientNet uses depth-wise separable convolution to minimize training parameters and capture relevant texture features to localize the tumor boundary accurately. The AC block handles the diversity in the shape and size of tumors. The classification network based on the pre-trained CNN architecture and fine-tuned using a custom neural network is developed. The classification network is used to classify the generated masks into benign, malignant, and normal classes. The performance of the proposed method is tested on two publicly available US image datasets, BUSI and dataset-B. The proposed method demonstrates a reduction in the number of parameters, a decrease in computational complexity, and a hike in inference speed while achieving superior performance in terms of segmentation and classification metrics.
Similar content being viewed by others
Data Availability
Not applicable.
Code Availability
Code would be available with the manuscript after it is accepted for publication.
References
Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A (2020) Dataset of breast ultrasound images. Data Brief 28:104863. https://doi.org/10.1016/j.dib.2019.104863
Araujo A, Norris W, Sim J (2019) Computing receptive fields of convolutional neural networks. https://distill.pub/2019/computing-receptive-fields
Balaha HM, Saif M, Tamer A, Abdelhay EH (2022) Hybrid deep learning and genetic algorithms approach (HMB-DLGAHA) for the early ultrasound diagnoses of breast cancer. Neural Comput Appl 34:8671–8695. https://doi.org/10.1007/s00521-021-06851-5
Byra M, Jarosik P, Szubert A, Galperin M, Ojeda-Fournier H, Olson L, O’Boyle M, Comstock C, Andre M (2020) Breast mass segmentation in ultrasound with selective kernel u-net convolutional neural network. Biomed Signal Process Control 61:102027
Byra M, Jarosik P, Szubert A, Galperin M, Ojeda-Fournier H, Olson L, O’Boyle M, Comstock C, Andre M (2022) Explaining a deep learning based breast ultrasound image classifier with saliency maps. J Ultrason 22:70–75. https://doi.org/10.15557/jou.2022.0013
Bäuerle A, van Onzenoodt C, Ropinski T (2021) Net2vis—a visual grammar for automatically generating publication-tailored CNN architecture visualizations. IEEE Trans Vis Comput Gr 27(6):2980–2991. https://doi.org/10.1109/TVCG.2021.3057483
Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587
Chollet F (2016) Xception: deep learning with depthwise separable convolutions. In: Proceedings—30th IEEE conference on computer vision and pattern recognition, CVPR 2017, 2017-January, pp 1800–1807. arXiv:1610.02357v3
Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2010) Imagenet: a large-scale hierarchical image database, n/a. In: Institute of Electrical and Electronics Engineers (IEEE), vol 3, pp 248–255. https://doi.org/10.1109/CVPR.2009.5206848
Du J, Guan K, Zhou Y, Li Y, Wang T (2022) Parameter-free similarity-aware attention module for medical image classification and segmentation. IEEE Trans Emerg Top Comput Intell. https://doi.org/10.1109/TETCI.2022.3199733
Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 7132–7141. https://doi.org/10.1109/CVPR.2018.00745
Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen Y-W, Wu J (2020) Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020—2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1055–1059. https://doi.org/10.1109/ICASSP40776.2020.9053405
Huang Q, Luo Y, Zhang Q (2017) Breast ultrasound image segmentation: a survey. Int J Comput Assist Radiol Surg 12:493–507. https://doi.org/10.1007/s11548-016-1513-1
Khan RA, Luo Y, Wu FX (2022) RMS-UNet: residual multi-scale unet for liver and lesion segmentation. Artif Intell Med 124:102–231. https://doi.org/10.1016/j.artmed.2021.102231
Kingma DP, Lei BJ (2014) Adam: a method for stochastic optimization. 12. arXiv:1412.6980v9
Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX, Ni D, Wang T (2019) Deep learning in medical ultrasound analysis: a review. Engineering 5:261–275. https://doi.org/10.1016/j.eng.2018.11.020
Mathur P, Sathishkumar K, Chaturvedi M, Das P, Sudarshan KL, Santhappan S, Nallasamy V, John A, Narasimhan S, Roselind FS (2020) Cancer statistics, 2020: report from national cancer registry programme, India. JCO Glob Oncol 2020:1063–1075. https://doi.org/10.1200/go.20.00122
Mishra AK, Roy P, Bandyopadhyay S, Das SK (2022) Feature fusion based machine learning pipeline to improve breast cancer prediction. Multimed Tools Appl 81:37627–37655. https://doi.org/10.1007/s11042-022-13498-4
Moon WK, Lee YW, Ke HH, Lee SH, Huang CS, Chang RF (2020) Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Comput Methods Programs Biomed 190:105361
Muduli D, Dash R, Majhi B (2022) Automated diagnosis of breast cancer using multi-modal datasets: a deep convolution neural network based approach. Biomed Signal Process Control 71:102825
Nassif AB, Talib MA, Nasir Q, Afadar Y, Elgendy O (2022) Breast cancer detection using artificial intelligence techniques: a systematic literature review. Artif Intell Med 127:102276. https://doi.org/10.1016/J.ARTMED.2022.102276
Ning Z, Tu C, Xiao Q, Luo J, Zhang Y (2020) Multi-scale gradational-order fusion framework for breast lesions classification using ultrasound images. In: Martel Anne L, Purang A, Danail S, Diana M, Zuluaga Maria A, Kevin ZS, Daniel R, Leo J (eds) Medical image computing and computer assisted intervention—MICCAI 2020. Springer, Cham, pp 171–180. https://doi.org/10.1007/978-3-030-59725-2_17
Ning Z, Zhong S, Feng Q, Chen W, Zhang Yu (2022) SMU-net: saliency-guided morphology-aware u-net for breast lesion segmentation in ultrasound image. IEEE Trans Med Imaging 41(2):476–490. https://doi.org/10.1109/TMI.2021.3116087
Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B (2018) Attention u-net: learning where to look for the pancreas. arXiv:1804.03999
Punn NS, Agarwal S (2022) RCA-IUnet: a residual cross-spatial attention-guided inception u-net model for tumor segmentation in breast ultrasound imaging. Mach Vis Appl 33:1–10. https://doi.org/10.1007/s00138-022-01280-3
Qin X, Zhang Z, Huang C, Dehghan M, Zaiane OR, Jagersand M (2020) U2-net: going deeper with nested u-structure for salient object detection. Pattern Recognit 106:107404
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation, pp 1–8. https://doi.org/10.1007/978-3-319-24574-4_28
Sadad T, Hussain A, Munir A, Habib M, Ali Khan S, Hussain S, Yang S, Alawairdhi M (2020) Identification of breast malignancy by marker-controlled watershed transformation and hybrid feature set for healthcare. Appl Sci 10(6):1900
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 4510–4520. arxiv:1801.04381v4
Sha Y (2021) Keras-unet-collection. https://github.com/yingkaisha/keras-unet-collection
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249
Tama BA, Vania M, Kim I, Lim S (2022) An efficientnet-based weighted ensemble model for industrial machine malfunction detection using acoustic signals. IEEE Access 10:34625–34636. https://doi.org/10.1109/ACCESS.2022.3160179
Tan M, Le QV (2019) Efficientnet: rethinking model scaling for convolutional neural networks. http://arxiv.org/abs/1905.11946
Tong Y, Liu Y, Zhao M, Meng L, Zhang J (2021) Improved U-net MALF model for lesion segmentation in breast ultrasound images. Biomed Signal Process Control 68:102721
Vakanski A, Xian M, Freer PE (2020) Attention-enriched deep learning model for breast tumor segmentation in ultrasound images. Ultrasound Med Biol 46(10):2819–2833
Wang Y, Ge X, Ma H, Qi S, Zhang G, Yao Y (2021) Deep learning in medical ultrasound image analysis: a review. IEEE Access 9:54310–54324. https://doi.org/10.1109/ACCESS.2021.3071301
Wei M, Du Y, Wu X, Su Q, Zhu J, Zheng L, Lv G, Zhuang J (2020) A benign and malignant breast tumor classification method via efficiently combining texture and morphological features on ultrasound images. Comput Math Methods Med. https://doi.org/10.1155/2020/5894010
Xing J, Chen C, Qinyang L, Cai X, Aijun Yu, Yi X, Xia X, Sun Y, Xiao J, Huang L (2021) Using bi-rads stratifications as auxiliary information for breast masses classification in ultrasound images. IEEE J Biomed Health Inform 25(6):2058–2070. https://doi.org/10.1109/JBHI.2020.3034804
Xu C, Qi Y, Wang Y, Lou M, Pi J, Ma Y (2022) Arf-net: an adaptive receptive field network for breast mass segmentation in whole mammograms and ultrasound images. Biomed Signal Process Control 71:103178
Yap MH, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Davison AK, Marti R (2018) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 22(4):1218–1226
Yu K, Chen S, Chen Y (2021) Tumor segmentation in breast ultrasound image by means of res path combined with dense connection neural network. Diagnostics 11:1565–1579
Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11045 LNCS, pp 3–11. https://doi.org/10.1007/978-3-030-00889-5_1
Zou H, Gong X, Luo J, Li T (2021) A robust breast ultrasound segmentation method under noisy annotations. Comput Methods Progr Biomed 209:106327. https://doi.org/10.1016/j.cmpb.2021.106327
Funding
The authors declare they have no financial interests.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethics Approval
No humans/animals were involved in the study.
Consent to Participate
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Dar, M.F., Ganivada, A. EfficientU-Net: A Novel Deep Learning Method for Breast Tumor Segmentation and Classification in Ultrasound Images. Neural Process Lett 55, 10439–10462 (2023). https://doi.org/10.1007/s11063-023-11333-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-023-11333-x