Qi X, Yi F, Zhang L, Chen Y, Pi Y, Chen Y, Guo J, Wang J, Guo Q, Li J, Chen Y. Computer-aided diagnosis of BC in ultrasonography images by deep learning. Neurocomputing. 2022;472:152–65. https://doi.org/10.1016/j.neucom.2021.11.047.

Article  Google Scholar 

Raza A, Ullah N, Khan JA, Assam M, Guzzo A, Aljuaid H. DeepBreastCancerNet: a novel deep learning model for BC detection using ultrasound images. Appl Sci. 2023;13(4):2082. https://doi.org/10.3390/app13042082.

Article  Google Scholar 

Jahwar AF, Abdulazeez AM. Segmentation and classification for BC ultrasound images using deep learning techniques: a review. In 2022 IEEE 18th International Colloquium on Signal Processing & Applications. 2022; 225–230. https://doi.org/10.1109/CSPA55076.2022.9781824

Xu S, Liu L, Zhao Z. DTFTCNet: radar modulation recognition with deep time-frequency transformation. IEEE Trans Cogn Commun Netw. 2023;9(5):1200–10. https://doi.org/10.1109/TCCN.2023.3280949.

Article  Google Scholar 

Uysal F, Köse MM. Classification of BC ultrasound images with deep learning-based models. Eng Proc. 2022;31(1):8. https://doi.org/10.3390/ASEC2022-13791.

Article  Google Scholar 

Ragab M, Albukhari A, Alyami J, Mansour RF. Ensemble deep-learning-enabled clinical decision support system for BC diagnosis and classification on ultrasound images. Biology. 2022;11(3):439. https://doi.org/10.3390/biology11030439.

Article  Google Scholar 

Zhang S, Liao M, Wang J, Zhu Y, Zhang Y, Zhang J, Zheng R, Lv L, Zhu D, Chen H, Wang W. Fully automatic tumor segmentation of breast ultrasound images with deep learning. J Appl Clin Med Phys. 2023;24(1):13863. https://doi.org/10.1002/acm2.13863.

Article  Google Scholar 

Li Y, Gu H, Wang H, Qin P, Wang J. BUSnet: a deep learning model of breast tumor lesion detection for ultrasound images. Front Oncol. 2022;12:848271. https://doi.org/10.3389/fonc.2022.848271.

Article  Google Scholar 

Luo Y, Huang Q, Li X. Segmentation information with attention integration for classification of breast tumor in ultrasound image. Pattern Recogn. 2022;124:108427. https://doi.org/10.1016/j.patcog.2021.108427.

Article  Google Scholar 

Dar RA, Rasool M, Assad A. BC detection using deep learning: Datasets, methods, and challenges ahead. Comput Biol Med 2022; 106073. https://doi.org/10.1016/j.compbiomed.2022.106073

Michael E, Ma H, Li H, Qi S. An optimized framework for BC classification using machine learning. BioMed Res Int. 2022;2022. https://doi.org/10.1155/2022/8482022

Hossain S, Azam S, Montaha S, Karim A, Chowa SS, Mondol C, Hasan MZ, Jonkman M. Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model. Heliyon. 2023;9(11). https://doi.org/10.1016/j.heliyon.2023.e21369

Tekin E, Yazıcı Ç, Kusetogullari H, Tokat F, Yavariabdi A, Iheme LO, Çayır S, Bozaba E, Solmaz G, Darbaz B, Özsoy G. Tubule-U-Net: a novel dataset and deep learning-based tubule segmentation framework in whole slide images of BC. Sci Rep. 2023;13(1):128.

Article  Google Scholar 

Iqbal A. Sharif M UNet: a semi-supervised method for segmentation of breast tumor images using a U-shaped pyramid-dilated network. Expert Syst Appl. 2023;221:119718. https://doi.org/10.1016/j.eswa.2023.119718.

Article  Google Scholar 

Hossain AA, Nisha JK, Johora F. BC classification from ultrasound images using VGG16 model based transfer learning. Int J Image Graph Signal Process. 2023;13:12.

Article  Google Scholar 

Cruz-Ramos C, García-Avila O, Almaraz-Damian JA, Ponomaryov V, Reyes-Reyes R, Sadovnychiy S. Benign and malignant breast tumor classification in ultrasound and mammography images via fusion of deep learning and handcraft features. Entropy. 2023;25(7):991. https://doi.org/10.3390/e25070991.

Article  Google Scholar 

Obayya M, Haj Hassine SB, Alazwari SK, Nour M, Mohamed A, Motwakel A, Yaseen I, Sarwar Zamani A, Abdelmageed AA, Mohammed GP. Aquila optimizer with Bayesian neural network for BC detection on ultrasound images. Appl Sci. 2022;12(17):8679. https://doi.org/10.3390/app12178679.

Article  Google Scholar 

Wang Y, Yao Y. Breast lesion detection using an anchor-free network from ultrasound images with segmentation-based enhancement. Sci Rep. 2022;12(1):14720. https://doi.org/10.1038/s41598-022-18747-y.

Article  Google Scholar 

Vigil N, Barry M, Amini A, Akhloufi M, Maldague XP, Ma L, Ren L, Yousefi B. Dual-intended deep learning model for BC diagnosis in ultrasound imaging. Cancers. 2022;14(11):2663.

Article  Google Scholar 

Iqbal A, Sharif M. BTS-ST: Swin transformer network for segmentation and classification of multimodality BC images. Knowl-Based Syst. 2023;267:110393. https://doi.org/10.1016/j.knosys.2023.110393.

Article  Google Scholar 

Yan Y, Liu Y, Wu Y, Zhang H, Zhang Y, Meng L. Accurate segmentation of breast tumors using AE U-net with HDC model in ultrasound images. Biomed Signal Process Control. 2022;72:103299. https://doi.org/10.1016/j.bspc.2021.103299.

Article  Google Scholar 

Jabeen K, Khan MA, Alhaisoni M, Tariq U, Zhang YD, Hamza A, Mickus A, Damaševičius R. BC classification from ultrasound images using probability-based optimal deep learning feature fusion. Sensors. 2022;22(3):807.

Article  Google Scholar 

Bourouis S, Band SS, Mosavi A, Agrawal S, Hamdi M. Meta-heuristic algorithm-tuned neural network for BC diagnosis using ultrasound images. Front Oncol. 2022;12:834028. https://doi.org/10.3389/fonc.2022.834028.

Article  Google Scholar 

Liu H, Cui G, Luo Y, Guo Y, Zhao L, Wang Y, Subasi A, Dogan S, Tuncer T. Artificial intelligence-based BC diagnosis using ultrasound images and grid-based deep feature generator. Int J Gen Med, 2022; 2271–2282.

Saba T, Abunadi I, Sadad T, Khan AR, Bahaj SA. Optimizing the transfer‐learning with pretrained deep convolutional neural networks for first stage breast tumor diagnosis using breast ultrasound visual images. Microsc Res Tech, 2022;85(4):1444–1453. https://doi.org/10.1002/jemt.2400810.1002/jemt.24008

Sahu A, Das PK, Meher S. High accuracy hybrid CNN classifiers for BC detection using mammogram and ultrasound datasets. Biomed Signal Process Control. 2023;80:104292. https://doi.org/10.1016/j.bspc.2022.104292.

Article  Google Scholar 

Vijayakumar K, Rajinikanth V, Kirubakaran MK. Automatic detection of BC in ultrasound images using Mayfly algorithm optimized handcrafted features. J Xray Sci Technol. 2022;30(4):751–66. https://doi.org/10.3233/XST-221136.

Article  Google Scholar 

Li Y, Liu Y, Huang L, Wang Z, Luo J. Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints. Med Image Anal. 2022;76:102315.

Article  Google Scholar 

Inan MSK, Alam FI, Hasan R. Deep integrated pipeline of segmentation guided classification of BC from ultrasound images. Biomed Signal Process Control. 2022;75:103553. https://doi.org/10.1016/j.bspc.2022.103553.

Article  Google Scholar 

Lu SY, Wang SH, Zhang YD. SAFNet: a deep spatial attention network with classifier fusion for BC detection. Comput Biol Med. 2022;148:105812. https://doi.org/10.1016/j.compbiomed.2022.105812.

Article  Google Scholar 

https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset

Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data Brief. 2020;28:104863.

Article  Google Scholar 

Alam T, Shia WC, Hsu FR, Hassan T. Improving BC detection and diagnosis through semantic segmentation using the Unet3+ deep learning framework. Biomedicines. 2023;11(6):1536. https://doi.org/10.3390/biomedicines11061536.

Article  Google Scholar 

Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(12):2481–95. https://doi.org/10.1109/TPAMI.2016.2644615.

Article  Google Scholar 

Zhang Z, Gao S, Huang Z. An automatic glioma segmentation system using a multilevel attention pyramid scene parsing network. Curr Med Imag. 2021;17(6):751–61. https://doi.org/10.2174/1573405616666201231100623.

Article  Google Scholar 

Ben Ahmed I, Ouarda W, Ben Amar C. Hybrid UNET Model Segmentation for an Early BC Detection Using Ultrasound Images. In International Conference on Computational Collective Intelligence. 2022;464–476. https://doi.org/10.1007/978-3-031-16014-1_37

Laghmati S, Hicham K, Cherradi B, Hamida S, Tmiri A. Segmentation of BC on Ultrasound Images using Attention U-Net Model. Int J Adv Comput Sci Appl. 2023;14(8).

Pramanik P, Roy A, Cuevas E, Perez-Cisneros M, Sarkar R. DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images. PLoS ONE. 2024;19(5):e0303670. https://doi.org/10.1371/journal.pone.0303670.

Article  Google Scholar 

Misra S, Yoon C, Kim KJ Managuli R, Barr RG, Baek J, Kim C. Deep learning‐based multimodal fusion network for segmentation and classification of BCs using B‐mode and elastography ultrasound images. Bioeng Transl Med, 2023;8(6):e10480. https://doi.org/10.1002/btm2.10480

Misra S, Jeon S, Managuli R, Lee S, Kim G, Yoon C, Lee S, Barr RG, Kim C. Bi-modal transfer learning for classifying BCs via combined b-mode and ultrasound strain imaging. IEEE Trans Ultrason Ferroelectr Freq Control. 2021;69(1):222–32. https://doi.org/10.1109/TUFFC.2021.3119251.

Article  Google Scholar 

Sivamurugan J, Sureshkumar G. Applying dual models on optimized LSTM with U-net segmentation for breast cancer diagnosis using mammogram images. Artif Intell Med. 2023;143:102626.

Article  Google Scholar