Skip to main content
Log in

A Review on Computational Methods for Breast Cancer Detection in Ultrasound Images Using Multi-Image Modalities

  • Review article
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

Breast cancer is a kind of cancer that develops and propagates from tissues of the breast and slowly transcends the whole body, this type of tumor is found in both sexes. Early detection of this disease is very important as at this stage it can be controlled by giving patients the required treatment and their valuable life can be saved. Researchers and scientists according to various studies have found methods to detect cancer at the initial stages, however, misperception in identifying skeptical lesions can be due to poor image quality and diverse breast density. Breast cancer (BC) is still a major concern for world health, necessitating ongoing innovation in early diagnosis and detection. Breast cancer diagnosis has made significant strides in recent years, especially with the incorporation of multi-modal imaging modalities. This article provides a summary of the most recent methods and advancements in multi-modal imaging for the detection of breast cancer. When radiomics, a quantitative study of imaging data, is integrated with machine learning and deep learning algorithms, breast lesions have demonstrated potential. These techniques can help distinguish between benign and malignant tumours, providing physicians with crucial information.At various phases of breast cancer detection, new methods have been developed for enhancement, segmentation, feature extraction, and classification employing multiple picture modalities. This review paper‘s objective is to represent all prior research in the area of breast cancer categorization utilising many imaging modalities. This paper provides a thorough and rigorous examination of current trends in the field of BC detection and classification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Wadhwa G, Mathur M (2020) A convolutional neural network approach for the diagnosis of breast cancer.” In: PDGC 2020–2020 6th International Conference on Parallel, Distributed and Grid Computing, Nov. 2020. p 357–361. doi: https://doi.org/10.1109/PDGC50313.2020.9315817.

  2. Saber A, Sakr M, Abo-Seida OM, Keshk A, Chen H (2021) A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access 9:71194–71209. https://doi.org/10.1109/ACCESS.2021.3079204

    Article  Google Scholar 

  3. Khan SU, Islam N, Jan Z, Ud Din I, Rodrigues JJPC (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning”. Pattern Recognit Lett. 125:1–6. https://doi.org/10.1016/j.patrec.2019.03.022

    Article  Google Scholar 

  4. Feng X et al (2019) Accurate prediction of neoadjuvant chemotherapy pathological complete remission (PCR) for the four sub-types of breast cancer. IEEE Access 7:134697–134706. https://doi.org/10.1109/ACCESS.2019.2941543

    Article  Google Scholar 

  5. Gangeh MJ, Liu S, Tadayyon H, Czarnota GJ (2018) Computer-aided theragnosis based on tumor volumetric information in breast cancer. IEEE Trans Ultrason Ferroelectr Freq Control 65(8):1359–1369. https://doi.org/10.1109/TUFFC.2018.2839714

    Article  Google Scholar 

  6. Gangeh MJ, Tadayyon H, Sannachi L, Sadeghi-Naini A, Tran WT, Czarnota GJ (2016) Computer aided theragnosis using quantitative ultrasound spectroscopy and maximum mean discrepancy in locally advanced breast cancer. IEEE Trans Med Imaging 35(3):778–790. https://doi.org/10.1109/TMI.2015.2495246

    Article  Google Scholar 

  7. McIntosh C, Purdie TG (2016) Contextual atlas regression forests: multiple-atlas-based automated dose prediction in radiation therapy. IEEE Trans Med Imaging 35(4):1000–1012. https://doi.org/10.1109/TMI.2015.2505188

    Article  Google Scholar 

  8. Fatima N, Liu L, Hong S, Ahmed H (2020) Prediction of breast cancer, comparative review of machine learning techniques, and their analysis. IEEE Access 8:150360–150376. https://doi.org/10.1109/ACCESS.2020.3016715

    Article  Google Scholar 

  9. Cowell CF et al (2013) Progression from ductal carcinoma in situ to invasive breast cancer: revisited. Mol Oncol 7(5):859–869. https://doi.org/10.1016/j.molonc.2013.07.005

    Article  Google Scholar 

  10. Celik Y, Talo M, Yildirim O, Karabatak M, Acharya UR (2020) Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recognit Lett 133:232–239. https://doi.org/10.1016/j.patrec.2020.03.011

    Article  Google Scholar 

  11. Dardick I, Peter van Nostrand AW, Phillips MJ (1982) Histogenesis of salivary gland pleomorphic adenoma (mixed tumor) with an evaluation of the role of the myoepithelial cell. Hum Pathol 13(1):62–75. https://doi.org/10.1016/S0046-8177(82)80140-8

    Article  Google Scholar 

  12. Kim S, Kim D, Choi HJ, Joo HJ (2011) “Detection of lobular carcinoma in situ(LCIS) by image analysis.” In: 2011 IEEE Int. Conf. Bioinforma. Biomed. Work. BIBMW 2011. p 623–624. doi: https://doi.org/10.1109/BIBMW.2011.6112440

  13. Memis A, Ozdemir N, Parildar M, Ustun EE, Erhan Y (2000) Mucinous (colloid) breast cancer: mammographic and US features with histologic correlation. Eur J Radiol 35(1):39–43. https://doi.org/10.1016/S0720-048X(99)00124-2

    Article  Google Scholar 

  14. Dawood S et al (2011) International expert panel on inflammatory breast cancer: consensus statement for standardized diagnosis and treatment. Ann Oncol 22(3):515–523. https://doi.org/10.1093/annonc/mdq345

    Article  Google Scholar 

  15. Yadav A, Verma VK, Pal V, Jain V, Garg V (2021) “Automated Detection and Classification of Breast Cancer Tumour Cells using Machine Learning and Deep Learning on Histopathological Images.” In: 2021 6th International Conference for Convergence in Technology, I2CT 2021. doi: https://doi.org/10.1109/I2CT51068.2021.9417996.

  16. Xu N, Li C (2020) Image feature extraction in detection technology of breast tumor. J King Saud Univ - Sci 32(3):2170–2175. https://doi.org/10.1016/j.jksus.2020.02.018

    Article  Google Scholar 

  17. Khasana U, Sigit R, Yuniarti H (2020) “Segmentation of Breast Using Ultrasound Image for Detection Breast Cancer.” IES 2020 Int. Electron. Symp. Role Auton. Intell. Syst. Hum. Life Comf. p 584–587. doi: https://doi.org/10.1109/IES50839.2020.9231629.

  18. Kretz T, Mueller KR, Schaeffter T, Elster C (2020) Mammography image quality assurance using deep learning. IEEE Trans Biomed Eng 67(12):3317–3326. https://doi.org/10.1109/TBME.2020.2983539

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Chen H, Ma M, Liu G, Wang Y, Jin Z, Liu C (2023) Breast tumor classification in ultrasound images by fusion of deep convolutional neural network and shallow LBP feature. J Digit Imaging 36(3):932–946. https://doi.org/10.1007/s10278-022-00711-x

    Article  Google Scholar 

  21. Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7):1455–1462. https://doi.org/10.1109/TBME.2015.2496264

    Article  Google Scholar 

  22. Benhammou Y, Achchab B, Herrera F, Tabik S (2020) BreakHis based breast cancer automatic diagnosis using deep learning: taxonomy, survey and insights. Neurocomputing 375:9–24. https://doi.org/10.1016/j.neucom.2019.09.044

    Article  Google Scholar 

  23. Bándi P et al (2019) From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge. IEEE Trans Med Imaging 38(2):550–560. https://doi.org/10.1109/TMI.2018.2867350

    Article  Google Scholar 

  24. Peikari M, Gangeh MJ, Zubovits J, Clarke G, Martel AL (2016) Triaging diagnostically relevant regions from pathology whole slides of breast cancer: a texture based approach. IEEE Trans Med Imaging 35(1):307–315. https://doi.org/10.1109/TMI.2015.2470529

    Article  Google Scholar 

  25. Vaka AR, Soni B (2020) Breast cancer detection by leveraging machine learning. ICT Express. 6(4):320–324. https://doi.org/10.1016/j.icte.2020.04.009

    Article  Google Scholar 

  26. Nikolaev AV et al (2021) Quantitative evaluation of an automated cone-based breast ultrasound scanner for MRI-3D US image fusion. IEEE Trans Med Imaging 40(4):1229–1239. https://doi.org/10.1109/TMI.2021.3050525

    Article  Google Scholar 

  27. Chen C, Wang Y, Niu J, Liu X, Li Q, Gong X (2021) Domain knowledge powered deep learning for breast cancer diagnosis based on contrast-enhanced ultrasound videos. IEEE Trans Med Imaging 40(9):2439–2451. https://doi.org/10.1109/TMI.2021.3078370

    Article  Google Scholar 

  28. Imran S, Lodhi BA, Alzahrani A (2021) Unsupervised method to localize masses in mammograms. IEEE Access 9:99327–99338. https://doi.org/10.1109/ACCESS.2021.3094768

    Article  Google Scholar 

  29. Selvathi ADA “Performance analysis of various classifiers on deep learning network for breast cancer detection.” In: 2017 International Conference on Signal Processing and Communication (ICSPC). IEEE. p 359–363

  30. Carneiro G, Nascimento J, Bradley AP (2017) Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans Med Imaging 36(11):2355–2365. https://doi.org/10.1109/TMI.2017.2751523

    Article  Google Scholar 

  31. Azour F, Boukerche A (2022) Design guidelines for mammogram-based computer-aided systems using deep learning techniques. IEEE Access 10:21701–21726. https://doi.org/10.1109/ACCESS.2022.3151830

    Article  Google Scholar 

  32. Wang Y, Feng Y, Zhang L, Wang Z, Lv Q, Yi Z (2021) Deep adversarial domain adaptation for breast cancer screening from mammograms. Med Image Anal 73:102147. https://doi.org/10.1016/j.media.2021.102147

    Article  Google Scholar 

  33. Sánchez-Cauce R, Pérez-Martín J, Luque M (2021) Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2021.106045

    Article  Google Scholar 

  34. Long R et al (2021) Improving the diagnostic accuracy of breast BI-RADS 4 microcalcification-only lesions using contrast-enhanced mammography. Clin Breast Cancer 21(3):256-262.e2. https://doi.org/10.1016/j.clbc.2020.10.011

    Article  Google Scholar 

  35. Kallenberg M et al (2016) Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging 35(5):1322–1331. https://doi.org/10.1109/TMI.2016.2532122

    Article  Google Scholar 

  36. Tan T, Platel B, Mus R, Tabar L, Mann RM, Karssemeijer N (2013) Computer-aided detection of cancer in automated 3-D breast ultrasound. IEEE Trans Med Imaging 32(9):1698–1706. https://doi.org/10.1109/TMI.2013.2263389

    Article  Google Scholar 

  37. Hendriks GAGM, Chen C, Hansen HHG, De Korte CL (2018) 3-D single breath-hold shear strain estimation for improved breast lesion detection and classification in automated volumetric ultrasound scanners. IEEE Trans Ultrason Ferroelectr Freq Control 65(9):1590–1599. https://doi.org/10.1109/TUFFC.2018.2849687

    Article  Google Scholar 

  38. Atrey K, Singh BK, Bodhey NK (2023) Multimodal classification of breast cancer using feature level fusion of mammogram and ultrasound images in machine learning paradigm. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-16414-6

    Article  Google Scholar 

  39. Dar MF, Ganivada A (2023) EfficientU-net: a novel deep learning method for breast tumor segmentation and classification in ultrasound images. Neural Process Lett. https://doi.org/10.1007/s11063-023-11333-x

    Article  Google Scholar 

  40. Zhou Y, Chen H, Li Y, Cao X, Wang S, Shen D (2022) Cross-model attention-guided tumor segmentation for 3D automated breast ultrasound (ABUS) images. IEEE J Biomed Heal Inf 26(1):301–311. https://doi.org/10.1109/JBHI.2021.3081111

    Article  Google Scholar 

  41. Ma G, Soleimani M (2020) Spectral capacitively coupled electrical resistivity tomography for breast cancer detection. IEEE Access 8:50900–50910. https://doi.org/10.1109/ACCESS.2020.2980112

    Article  Google Scholar 

  42. Li Q et al (2015) Direct extraction of tumor response based on ensemble empirical mode decomposition for image reconstruction of early breast cancer detection by UWB. IEEE Trans Biomed Circuits Syst 9(5):710–724. https://doi.org/10.1109/TBCAS.2015.2481940

    Article  Google Scholar 

  43. Nyayapathi N et al (2020) Dual scan mammoscope (DSM)—a new portable photoacoustic breast imaging system with scanning in craniocaudal plane. IEEE Trans Biomed Eng 67(5):1321–1327. https://doi.org/10.1109/TBME.2019.2936088

    Article  Google Scholar 

  44. Wang X, Qin T, Witte RS, Xin H (2015) Computational feasibility study of contrast-enhanced thermoacoustic imaging for breast cancer detection using realistic numerical breast phantoms. IEEE Trans Microw Theory Tech 63(5):1489–1501. https://doi.org/10.1109/TMTT.2015.2417866

    Article  Google Scholar 

  45. Chiu HJ, Li THS, Kuo PH (2020) Breast cancer–detection system using PCA, multilayer perceptron, transfer learning, and support vector machine. IEEE Access 8:204309–204324. https://doi.org/10.1109/ACCESS.2020.3036912

    Article  Google Scholar 

  46. Sichuan Province Computer Federation and Institute of Electrical and Electronics Engineers, 2019 2nd International Conference on Artificial Intelligence and Big Data : ICAIBD 2019 : May 25–28, 2019, Chengdu, China.

  47. Singla C, Sarangi PK, Sahoo AK, Singh PK (2020) Deep learning enhancement on mammogram images for breast cancer detection. Mater. Today Proc. 49:3098–3104. https://doi.org/10.1016/j.matpr.2020.10.951

    Article  Google Scholar 

  48. Beeravolu AR, Azam S, Jonkman M, Shanmugam B, Kannoorpatti K, Anwar A (2021) Preprocessing of breast cancer images to create datasets for Deep-CNN. IEEE Access 9:33438–33463. https://doi.org/10.1109/ACCESS.2021.3058773

    Article  Google Scholar 

  49. Huang Q, Miao Z, Zhou S, Chang C, Li X (2021) Dense prediction and local fusion of superpixels: a framework for breast anatomy segmentation in ultrasound image with scarce data. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2021.3088421

    Article  Google Scholar 

  50. Qi X, Xing F, Foran DJ, Yang L (2012) Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. IEEE Trans Biomed Eng 59(3):754–765. https://doi.org/10.1109/TBME.2011.2179298

    Article  Google Scholar 

  51. Zhou Y et al (2018) A radiomics approach with CNN for shear-wave elastography breast tumor classification. IEEE Trans Biomed Eng 65(9):1935–1942. https://doi.org/10.1109/TBME.2018.2844188

    Article  Google Scholar 

  52. Zhang J, Saha A, Zhu Z, Mazurowski MA (2019) Hierarchical convolutional neural networks for segmentation of breast tumors in MRI with application to radiogenomics. IEEE Trans Med Imaging 38(2):435–447. https://doi.org/10.1109/TMI.2018.2865671

    Article  Google Scholar 

  53. Beevi KS, Nair MS, Bindu GR (2017) A multi-classifier system for automatic mitosis detection in breast histopathology images using deep belief networks. IEEE J. Transl. Eng. Heal. Med. 5:1–11. https://doi.org/10.1109/JTEHM.2017.2694004

    Article  Google Scholar 

  54. Kumar A et al (2021) CoMHisP: a novel feature extractor for histopathological image classification based on fuzzy SVM with within-class relative density. IEEE Trans Fuzzy Syst 29(1):103–117. https://doi.org/10.1109/TFUZZ.2020.2995968

    Article  Google Scholar 

  55. Vo DM, Nguyen NQ, Lee SW (2019) Classification of breast cancer histology images using incremental boosting convolution networks. Inf Sci (Ny) 482:123–138. https://doi.org/10.1016/j.ins.2018.12.089

    Article  Google Scholar 

  56. Elmoufidi A (2022) Deep multiple instance learning for automatic breast cancer assessment using digital mammography. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2022.3177141

    Article  Google Scholar 

  57. Quellec G, Lamard M, Cozic M, Coatrieux G, Cazuguel G (2016) Multiple-instance learning for anomaly detection in digital mammography. IEEE Trans Med Imaging 35(7):1604–1614. https://doi.org/10.1109/TMI.2016.2521442

    Article  Google Scholar 

  58. Sarkar JP, Saha I, Sarkar A, Maulik U (2021) Machine learning integrated ensemble of feature selection methods followed by survival analysis for predicting breast cancer subtype specific miRNA biomarkers. Comput. Biol. Med. 131:104244. https://doi.org/10.1016/j.compbiomed.2021.104244

    Article  Google Scholar 

  59. Toğaçar M, Özkurt KB, Ergen B, Cömert Z (2020) BreastNet: a novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Phys A Stat Mech its Appl 545:123592. https://doi.org/10.1016/j.physa.2019.123592

    Article  Google Scholar 

  60. Kongunadu College of Engineering & Technology and Institute of Electrical and Electronics Engineers, Proceedings, International Conference on Smart Electronics and Communication (ICOSEC 2020): 10–12, September 2020.

  61. Saranya S, Sasikala S (2020) “Diagnosis using data mining algorithms for malignant breast cancer cell detection.” In: Proceedings of the 4th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2020. p 1062–1067. doi: https://doi.org/10.1109/ICECA49313.2020.9297481.

  62. Oyelade ON, Ezugwu AES (2020) A state-of-the-art survey on deep learning methods for detection of architectural distortion from digital mammography. IEEE Access 8:148644–148676. https://doi.org/10.1109/ACCESS.2020.3016223

    Article  Google Scholar 

  63. Goni MOF, Hasnain FMS, Siddique MAI, Jyoti O, Rahaman MH (2020) “Breast Cancer Detection using Deep Neural Network.” In: ICCIT 2020—23rd International Conference on Computer and Information Technology, Proceedings. doi: https://doi.org/10.1109/ICCIT51783.2020.9392705.

  64. Lei H, Liu S, Elazab A, Gong X, Lei B (2021) Attention-guided multi-branch convolutional neural network for mitosis detection from histopathological images. IEEE J Biomed Heal Informatics 25(2):358–370. https://doi.org/10.1109/JBHI.2020.3027566

    Article  Google Scholar 

  65. Li M (2021) “Research on the detection method of breast cancer deep convolutional neural network based on computer aid.” In: Proceedings of IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021. p 536–540. doi: https://doi.org/10.1109/IPEC51340.2021.9421338.

  66. Arya N, Saha S (2022) Multi-modal classification for human breast cancer prognosis prediction: proposal of deep-learning based stacked ensemble model. IEEE/ACM Trans Comput Biol Bioinforma 19(2):1032–1041. https://doi.org/10.1109/TCBB.2020.3018467

    Article  Google Scholar 

  67. Peng C, Zheng Y, Huang DS (2020) Capsule network based modeling of multi-omics data for discovery of breast cancer-related genes. IEEE/ACM Trans Comput Biol Bioinforma 17(5):1605–1612. https://doi.org/10.1109/TCBB.2019.2909905

    Article  Google Scholar 

  68. Yap MH et al (2018) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Heal Informatics 22(4):1218–1226. https://doi.org/10.1109/JBHI.2017.2731873

    Article  Google Scholar 

  69. Deb SD, Jha RK (2023) Breast UltraSound Image classification using fuzzy-rank-based ensemble network. Biomed. Signal Process. Control 85:104871. https://doi.org/10.1016/j.bspc.2023.104871

    Article  Google Scholar 

  70. Sirjani N et al (2023) A novel deep learning model for breast lesion classification using ultrasound Images: a multicenter data evaluation. Phys. Medica 107:102560. https://doi.org/10.1016/j.ejmp.2023.102560

    Article  Google Scholar 

  71. Goudarzi S, Whyte J, Boily M, Towers A, Kilgour RD, Rivaz H (2023) Segmentation of arm ultrasound images in breast cancer-related lymphedema: a database and deep learning algorithm. IEEE Trans Biomed Eng 70(9):2552–2563. https://doi.org/10.1109/TBME.2023.3253646

    Article  Google Scholar 

  72. Al-Juniad AF, Qaid TS, Al-Shamri MYH, Ahmed MHA, Raweh AA (2018) Vertical and horizontal DNA differential methylation analysis for predicting breast cancer. IEEE Access 6:53533–53545. https://doi.org/10.1109/ACCESS.2018.2871027

    Article  Google Scholar 

  73. Man R, Yang P, Xu B (2020) Classification of breast cancer histopathological images using discriminative patches screened by generative adversarial networks. IEEE Access 8:155362–155377. https://doi.org/10.1109/ACCESS.2020.3019327

    Article  Google Scholar 

  74. Hirra I et al (2021) Breast cancer classification from histopathological images using patch-based deep learning modeling. IEEE Access 9:24273–24287. https://doi.org/10.1109/ACCESS.2021.3056516

    Article  Google Scholar 

  75. Das K, Conjeti S, Chatterjee J, Sheet D (2020) Detection of breast cancer from whole slide histopathological images using deep multiple instance CNN. IEEE Access 8:213502–213511. https://doi.org/10.1109/ACCESS.2020.3040106

    Article  Google Scholar 

  76. Li G, Li C, Wu G, Ji D, Zhang H (2021) Multi-view attention-guided multiple instance detection network for interpretable breast cancer histopathological image diagnosis. IEEE Access 9:79671–79684. https://doi.org/10.1109/ACCESS.2021.3084360

    Article  Google Scholar 

  77. Shu X, Zhang L, Wang Z, Lv Q, Yi Z (2020) Deep neural networks with region-based pooling structures for mammographic image classification. IEEE Trans Med Imaging 39(6):2246–2255. https://doi.org/10.1109/TMI.2020.2968397

    Article  Google Scholar 

  78. Bacha S, Taouali O (2022) A novel machine learning approach for breast cancer diagnosis. Meas. J. Int. Meas. Confed. 187:110233. https://doi.org/10.1016/j.measurement.2021.110233

    Article  Google Scholar 

  79. Homayoun H et al (2022) Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: a multi-center study. Biocybern Biomed Eng 42(3):921–933. https://doi.org/10.1016/j.bbe.2022.07.004

    Article  Google Scholar 

  80. Rahman MM, Ghasemi Y, Suley E, Zhou Y, Wang S, Rogers J (2021) Machine learning based computer aided diagnosis of breast cancer utilizing anthropometric and clinical features. Irbm 42(4):215–226. https://doi.org/10.1016/j.irbm.2020.05.005

    Article  Google Scholar 

  81. Heidari M et al (2021) Applying a random projection algorithm to optimize machine learning model for breast lesion classification. IEEE Trans Biomed Eng 68(9):2764–2775. https://doi.org/10.1109/TBME.2021.3054248

    Article  Google Scholar 

  82. Gopal VN, Al-Turjman F, Kumar R, Anand L, Rajesh M (2021) Feature selection and classification in breast cancer prediction using IoT and machine learning. Meas. J. Int. Meas. Confed. 178:109442. https://doi.org/10.1016/j.measurement.2021.109442

    Article  Google Scholar 

  83. Liu X et al (2022) Predicting breast cancer recurrence and metastasis risk by integrating color and texture features of histopathological images and machine learning technologies. Comput. Biol. Med. 146:105569. https://doi.org/10.1016/j.compbiomed.2022.105569

    Article  Google Scholar 

  84. Zhang L et al (2022) “Raman spectroscopy and machine learning for the classification of breast cancers.” Spectrochim Acta Part A Mol. Biomol. Spectrosc. 264:120300. https://doi.org/10.1016/j.saa.2021.120300

    Article  Google Scholar 

  85. Zhang X, Liu W, Dundar M, Badve S, Zhang S (2015) Towards large-scale histopathological image analysis: hashing-based image retrieval. IEEE Trans Med Imaging 34(2):496–506. https://doi.org/10.1109/TMI.2014.2361481

    Article  Google Scholar 

  86. Hou R et al (2022) Anomaly detection of calcifications in mammography based on 11,000 negative cases. IEEE Trans Biomed Eng 69(5):1639–1650. https://doi.org/10.1109/TBME.2021.3126281

    Article  Google Scholar 

  87. Zhang X et al (2018) Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Trans Nanobioscience 17(3):237–242. https://doi.org/10.1109/TNB.2018.2845103

    Article  Google Scholar 

  88. Lopez-Almazan H et al (2022) A deep learning framework to classify breast density with noisy labels regularization. Comput Methods Programs Biomed 221:106885. https://doi.org/10.1016/j.cmpb.2022.106885

    Article  Google Scholar 

  89. Liu Z et al (2021) Axillary lymph node metastasis prediction by contrast-enhanced computed tomography images for breast cancer patients based on deep learning. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2021.104715

    Article  Google Scholar 

  90. Zheng J, Lin D, Gao Z, Wang S, He M, Fan J (2020) Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis. IEEE Access 8:96946–96954. https://doi.org/10.1109/ACCESS.2020.2993536

    Article  Google Scholar 

  91. Das A, Mohanty MN, Mallick PK, Tiwari P, Muhammad K, Zhu H (2021) Breast cancer detection using an ensemble deep learning method. Biomed. Signal Process. Control 70:103009. https://doi.org/10.1016/j.bspc.2021.103009

    Article  Google Scholar 

  92. Barsha NA, Rahman A, Mahdy MRC (2021) Automated detection and grading of invasive ductal carcinoma breast cancer using ensemble of deep learning models. Comput. Biol. Med. 139:104931. https://doi.org/10.1016/j.compbiomed.2021.104931

    Article  Google Scholar 

  93. Saha M, Chakraborty C, Racoceanu D (2018) Efficient deep learning model for mitosis detection using breast histopathology images. Comput. Med. Imaging Graph. 64:29–40. https://doi.org/10.1016/j.compmedimag.2017.12.001

    Article  Google Scholar 

  94. Graham S, Epstein D, Rajpoot N (2020) Dense steerable filter CNNs for exploiting rotational symmetry in histology images. IEEE Trans Med Imaging 39(12):4124–4136. https://doi.org/10.1109/TMI.2020.3013246

    Article  Google Scholar 

  95. Liu J et al (2019) An end-to-end deep learning histochemical scoring system for breast cancer TMA. IEEE Trans Med Imaging 38(2):617–628. https://doi.org/10.1109/TMI.2018.2868333

    Article  Google Scholar 

  96. Yari Y, Nguyen TV, Nguyen HT (2020) Deep learning applied for histological diagnosis of breast cancer. IEEE Access 8:162432–162448. https://doi.org/10.1109/ACCESS.2020.3021557

    Article  Google Scholar 

  97. Misra S et al (2022) “Bi-modal transfer learning for classifying breast cancers via combined B-mode and ultrasound strain imaging. IEEE Trans. Ultrason. Ferroelect. Freq. Contr. 69(1):222–232

    Article  Google Scholar 

  98. Brancati N, De Pietro G, Frucci M, Riccio D (2019) A deep learning approach for breast invasive ductal carcinoma detection and lymphoma multi-classification in histological images. IEEE Access. 7:44709–44720. https://doi.org/10.1109/ACCESS.2019.2908724

    Article  Google Scholar 

  99. Wu Y, Wu J, Dou Y, Rubert N, Wang Y, Deng J (2022) A deep learning fusion model with evidence-based confidence level analysis for differentiation of malignant and benign breast tumors using dynamic contrast enhanced MRI. Biomed. Signal Process. Control. 72:103319. https://doi.org/10.1016/j.bspc.2021.103319

    Article  Google Scholar 

  100. Zhou X, et al. (2020) “A new deep convolutional neural network model for automated breast cancer detection.” In: Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020. doi: https://doi.org/10.1109/BESC51023.2020.9348322.

  101. Özkurt N, Yıldırım T, Yaşar Üniversitesi (2019) Institute of Electrical and Electronics Engineers. Turkey Section., and Institute of Electrical and Electronics Engineers, 2019 Innovations in Intelligent Systems and Applications Conference (ASYU) : proceedings : 31 October-2 November 2019, Izmir, Turkey.

  102. Mohaiminul Islam M, Huang S, Ajwad R, Chi C, Wang Y, Hu P (2020) An integrative deep learning framework for classifying molecular subtypes of breast cancer. Comput. Struct. Biotechnol. J. 18:2185–2199. https://doi.org/10.1016/j.csbj.2020.08.005

    Article  Google Scholar 

  103. Araujo T et al (2017) Classification of breast cancer histology images using convolutional neural networks. PLoS One. https://doi.org/10.1371/journal.pone.0177544

    Article  Google Scholar 

  104. Bardou D, Zhang K, Ahmad SM (2018) Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access 6:24680–24693. https://doi.org/10.1109/ACCESS.2018.2831280

    Article  Google Scholar 

  105. Kashyap R (2022) Dilated residual grooming kernel model for breast cancer detection. Pattern Recognit Lett 159:157–164. https://doi.org/10.1016/j.patrec.2022.04.037

    Article  Google Scholar 

  106. Basavanhally A et al (2013) Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides. IEEE Trans Biomed Eng 60(8):2089–2099. https://doi.org/10.1109/TBME.2013.2245129

    Article  Google Scholar 

  107. Elbashir MK, Ezz M, Mohammed M, Saloum SS (2019) Lightweight convolutional neural network for breast cancer classification using RNA-Seq gene expression data. IEEE Access 7:185338–185348. https://doi.org/10.1109/ACCESS.2019.2960722

    Article  Google Scholar 

  108. Wang Y et al (2020) Breast cancer image classification via multi-network features and dual-network orthogonal low-rank learning. IEEE Access 8:27779–27792. https://doi.org/10.1109/ACCESS.2020.2964276

    Article  Google Scholar 

  109. Talukder MA, Islam MM, Uddin MA, Akhter A, Hasan KF, Moni MA (2022) Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning. Expert Syst. Appl. 205:117695. https://doi.org/10.1016/j.eswa.2022.117695

    Article  Google Scholar 

  110. Shin SY, Lee S, Yun ID, Kim SM, Lee KM (2019) Joint weakly and semi-supervised deep learning for localization and classification of masses in breast ultrasound images. IEEE Trans Med Imaging 38(3):762–774. https://doi.org/10.1109/TMI.2018.2872031

    Article  Google Scholar 

  111. Kaur P, Singh G, Kaur P (2019) Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Informatics Med Unlocked. https://doi.org/10.1016/j.imu.2019.01.001

    Article  Google Scholar 

  112. Wang P et al (2020) Cross-task extreme learning machine for breast cancer image classification with deep convolutional features. Biomed Signal Process Control 57:101789. https://doi.org/10.1016/j.bspc.2019.101789

    Article  Google Scholar 

  113. Don S, Chung D, Revathy K, Choi E, Min D (2009) A neural network approach to mammogram image classification using fractal features. Proc. 2009 IEEE Int Conf. Intell. Comput. Intell. Syst. ICIS 2009. 4:444–447. https://doi.org/10.1109/ICICISYS.2009.5357653

    Article  Google Scholar 

  114. Fatakdawala H et al (2010) Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR): application to lymphocyte segmentation on breast cancer histopathology. IEEE Trans Biomed Eng 57(7):1676–1689. https://doi.org/10.1109/TBME.2010.2041232

    Article  Google Scholar 

  115. Ting FF, Sim KS, Chong SS (2017) Auto-probing Breast Cancer Mass Segmentation for Early Detection”. In: 2017 International Conference on Robotics, Automation and Sciences (ICORAS). IEEE p 1–5

  116. von Lavante E, Noble JA (2008) Segmentation of breast cancer masses in ultrasound using radio-frequency signal derived parameters and strain estimates. In: 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE. p 536–539

  117. Pramanik S, Ghosh S, Bhattacharjee D, Nasipuir M (2020) Segmentation of breast-region in breast thermogram using arc-approximation and triangular-space search. IEEE Trans Instrum Meas 69(7):4785–4795. https://doi.org/10.1109/TIM.2019.2956362

    Article  Google Scholar 

  118. Kirthika A, Madhava Raja NS, Sivakumar R, Arunmozhi S (2020) Assesment of Tumor in Breast MRI using Kapur’s Thresholding and Active Contour Segmentation.” In: 2020 Int. Conf. Syst. Comput. Autom. Networking, ICSCAN 2020. p 15–18. doi: https://doi.org/10.1109/ICSCAN49426.2020.9262402.

  119. Prakash RM (2017) Segmentation of thermal infrared breast images using K-Means, FCM and EM algorithms for breast cancer detection. In: 2017 International conference on innovations in information, embedded and communication systems (ICIIECS). IEEE. p 1–4

  120. Chattaraj A, Das A, Bhattacharya M (2017) Mammographic Image Segmentation by Marker Controlled Watershed Algorithm. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE. p. 1000–1003

Download references

Acknowledgements

The authors thankfully acknowledge Ms. Preity, Ph.D. scholar in the ECE department, National Institute of Technology Patna, India for her throughout help during revision process and manuscript writing.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ashish Kumar Bhandari.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sushanki, S., Bhandari, A.K. & Singh, A.K. A Review on Computational Methods for Breast Cancer Detection in Ultrasound Images Using Multi-Image Modalities. Arch Computat Methods Eng 31, 1277–1296 (2024). https://doi.org/10.1007/s11831-023-10015-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-023-10015-0

Navigation