A Survey on Machine Learning Algorithms for the Diagnosis of Breast Masses with Mammograms

Author(s): Vaira Suganthi Gnanasekaran*, Sutha Joypaul, Parvathy Meenakshi Sundaram

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 16 , Issue 6 , 2020

Become EABM
Become Reviewer

Graphical Abstract:


Breast cancer is leading cancer among women for the past 60 years. There are no effective mechanisms for completely preventing breast cancer. Rather it can be detected at its earlier stages so that unnecessary biopsy can be reduced. Although there are several imaging modalities available for capturing the abnormalities in breasts, mammography is the most commonly used technique, because of its low cost. Computer-Aided Detection (CAD) system plays a key role in analyzing the mammogram images to diagnose the abnormalities. CAD assists the radiologists for diagnosis. This paper intends to provide an outline of the state-of-the-art machine learning algorithms used in the detection of breast cancer developed in recent years. We begin the review with a concise introduction about the fundamental concepts related to mammograms and CAD systems. We then focus on the techniques used in the diagnosis of breast cancer with mammograms.

Keywords: Breast cancer, mammograms, computer-aided detection, genetic algorithm, deep learning, neural networks.

NCI Dictionary of Cancer Terms Available from:. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/cancer
RadiologyInfoorg Available from:. https://www.radiologyinfo.org/
Martini N, Koukou V, Fountos G, et al. Characterization of breast calcification types using dual energy x-ray method. Phys Med Biol 2017; 62(19): 7741-64.
[http://dx.doi.org/10.1088/1361-6560/aa8445] [PMID: 28777746]
What is breast cancer? American Cancer Society Available from:. https://www.cancer.org/cancer/breast-cancer/about/what-is-breast-cancer.html
Halls S. A discussion of conventional mammography 2019.Available from:. https://breast-cancer.ca/mammopics/
Wishart GC, Campisi M, Boswell M, et al. The accuracy of digital infrared imaging for breast cancer detection in women undergoing breast biopsy. Eur J Surg Oncol 2010; 36(6): 535-40.
[http://dx.doi.org/10.1016/j.ejso.2010.04.003] [PMID: 20452740]
Sree SV, Ng EY-K, Acharya RU, Faust O. Breast imaging: A survey. World J Clin Oncol NCBI 2011; 2(4): 171-8.
National Library of Medicine Available from:. https://medlineplus.gov/mammography.html
Sampat MP, Markey MK, Bovik AC. Computer-aided detection and diagnosis in mammography handbook of image and video processing. London, U.K.: Elsevier 2003.
Hassanien EA, Gaber T. Handbook of research on machine learning innovations and trends A volume in the Advances in Computational Intelligence and Robotics (ACIR) book series. Pennsylvania: IGI Global 2017.
Tang J, Rangayyan RM, Xu J, El Naqa I, Yang Y. Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans Inf Technol Biomed 2009; 13(2): 236-51.
[http://dx.doi.org/10.1109/TITB.2008.2009441] [PMID: 19171527]
Ganesan K, Acharya UR, Chua CK, Min LC, Abraham KT, Ng KH. Computer-aided breast cancer detection using mammograms: a review. IEEE Rev Biomed Eng 2013; 6: 77-98.
[http://dx.doi.org/10.1109/RBME.2012.2232289] [PMID: 23247864]
Bedi SS, Khandelwa R. Various Image enhancement techniques-a critical review. Int J Adv Res Comput Commun Eng 2013; 2(3): 1605-9.
Selvathi D, Aarthy Poornila A. In: Hemanth J, Balas V, Eds. Deep learning techniques for breast cancer detection using medical image analysis biologically rationalized computing techniques for image processing applications. Cham: Springer 2018; pp. 159-86.
Abubacker NF, Azman A, Masrah AAM, Doraisamy S. An improved peripheral enhancement of mammogram images by using filtered region growing segmentation. J Theore Appl Informa Technol 2017; 95(14): 3270-80.
Pavitha R, Hephzibah JS. Mammographic cancer detection and classification using bi clustering and supervised classifier. Int J Innov Res Sci Engineer Technol 2014; 3(1): 1382-9.
Karnan M, Thangavel K. Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications. Comput Methods Programs Biomed 2007; 87(1): 12-20.
Kus P, Karagoz I. Fully automated gradient based breast boundary detection for digitized X-ray mammograms. Comput Biol Med 2012; 42(1): 75-82.
[http://dx.doi.org/10.1016/j.compbiomed.2011.10.011] [PMID: 22118773]
Vikhe PS, Thool VR. Intensityn based automatic boundary identification of pectoral muscle in mammograms. Procedia Comput Sci 2016; 79: 262-9.
Guliat D, Rangaraj M Rangayyan, Walter A Carniell, Joao A Zuff, Leo Desautels J E. Segmentation of Breast Tumors in Mammograms by Fuzzy Region Growing. In: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Hong Kong, China. 1998; 1002-5.
Meenakshi M. Local entropy maximization based image fusion for contrast enhancement of mammogram.J King Saud Univ Comp Inform Sci. 2016; 8: pp. 247-50.
Singh N, Ambarish G. Mohapatra. Breast cancer mass detection in mammograms using K-means and fuzzy C-means clustering. Int J Comput Appl 2011; 22(2): 15-21.
Senthilkumar B, Umamaheswari G. New computer-aided detection method for the effective detection of breast cancer. Online J Biol Sci 2012; 12(4): 156-60.
Pereira DC, Ramos RP, do Nascimento MZ. Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Comput Methods Programs Biomed 2014; 114(1): 88-101.
[http://dx.doi.org/10.1016/j.cmpb.2014.01.014] [PMID: 24513228]
Al-Najdawi N, Biltwani M, et al. Mammogram image visual enhancement, mass segmentation and classification. Appl Soft Comput 2015; 35(1): 175-85.
Bhateja V, Misra M, et al. A robust polynomial filtering framework for mammographic image enhancement from biomedical sensors. IEEE Sens J 2013; 13(11): 4147-56.
Gopal GN. Kanaga GN. A study on enhancement techniques for mammogram images. Int J Adv Res Electron Comm Engineer 2013; 2(1): 36-9.
Taghanaki SA, Liu Y, Miles B, Hamarneh G. Geometry based Pectoral Muscle Segmentation from MLO mammogram views. IEEE Trans Biomed Eng 2017; 64(11): 2662-71.
[http://dx.doi.org/10.1109/TBME.2017.2649481] [PMID: 28129144]
Nithya R, Santhi B. Mammogram analysis based on pixel intensity mean features. J Comput Sci 2012; 8(3): 329-32.
Goudarzi M, Maghooli K. Extraction of fuzzy rules at different levels related to image features of mammography for diagnosis of breast cancer. Biocybern Biomed Eng 2018; 38(4): 1004-14.
Sami D, Walid B, Ezzeddine Z. Breast cancer diagnosis in digitized mammograms using curvelet moments. Comp Biol Med 2015; 64(1): 79-90.
Rabidas R, Midya A, Chakraborty J, Arif W. A study of different texture features based on the local operator for benign-malignant mass classification. Procedia Comput Sci 2016; 93: 389-95.
Nag K, Pal NR. A multiobjective genetic programming-based ensemble for simultaneous feature selection and classification. IEEE Trans Cybern 2016; 46(2): 499-510.
[http://dx.doi.org/10.1109/TCYB.2015.2404806] [PMID: 25769178]
Samulski M, Karssemeijer N. Optimizing Case-based detection performance in a multiview CAD system for mammography. IEEE Trans Med Imaging 2011; 30(4): 1001-9.
[http://dx.doi.org/10.1109/TMI.2011.2105886] [PMID: 21233045]
Beheshti SMA. Classification of abnormalities in mammograms by new asymmetric fractal features. Biocybern Biomed Eng 2015; 36(1): 56-65.
Hayashi Y, Nakano S. Use of a recursive-rule eXtraction algorithm with J48graft to achieve highly accurate and concise rule extraction from a large breast cancer dataset. Inform Med Unlock 2015; 1: 9-16.
Kallenberg M, Petersen K, Nielsen M, et al. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging 2016; 35(5): 1322-31.
[http://dx.doi.org/10.1109/TMI.2016.2532122] [PMID: 26915120]
Hoe KD, Young CJ, Yong MR. Region based stellate features combined with variable selection using AdaBoost learning in mammographic computer-aided detection. Comput Biol Med 2015; 63: 238-50.
Abdel-Nasser M, Rashwan HA, Puig D, Moreno A. Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern. Expert Syst Appl 2015; 42(4): 9499-511.
Wajid, Amir hussain. Local energy-based shape feature extraction technique for breast cancer diagnosis. Expert Syst Appl 2015; 42(1): 6990-9.
Kotsiantis SB. Supervised machine learning: a review of classification techniques. Informatica 2007; 2017: 249-68.
Niranjan J. A Survey on Various Classification Techniques for Medical Image Data. Int J Comput Appl 2017; 97(15): 1-5.
Wu S-H, Lin K-P, et al. On generalizable low false-positive learning using asymmetric support vector machines. IEEE Trans Knowl Data Eng 2013; 25(5): 340-50.
Mc Leod P, Verma B. Variable hidden neuron ensemble for mass classification digital mammograms. IEEE Comput Intell Mag 2013; 2013: 68-76.
Tai S-C, Chen Z-S, Tsai WT. An automatic mass detection system in mammograms based on complex texture features. IEEE J Biomed Health Inform 2014; 18(2): 618-27.
[http://dx.doi.org/10.1109/JBHI.2013.2279097] [PMID: 24608061]
Shradhanda B. Mammogram classification using a two-dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing 2015; 154(1): 1-14.
Liu X, Tang J. Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method. IEEE Syst J 2014; 8(3): 910-20.
Bekker AJ, Shalhon M, Greenspan H, Goldberger J. Multi-view probabilistic classification of breast microcalcifications. IEEE Trans Med Imaging 2016; 35(2): 645-53.
[http://dx.doi.org/10.1109/TMI.2015.2488019] [PMID: 26452277]
Singh AK, Gupta B. Novel approach for breast cancer detection and segmentation in a mammogram. Procedia Comput Sci 2015; 54: 676-82.
Andreadis II, Spyrou GM, Nikita KS. A CADx scheme for mammography empowered with topological information from clustered microcalcifications’ atlases. IEEE J Biomed Health Inform 2015; 19(1): 166-73.
[http://dx.doi.org/10.1109/JBHI.2014.2334491] [PMID: 25073178]
Zhong X, Li J, Ertl SM, Hassemer C, Fiedler L. A system-theoretic approach to modeling and analysis of mammography testing process. IEEE Trans Syst Man Cybern Syst 2016; 46(1): 126-38.
Zhao W, Zhang J, Li K. An efficient LS-SVM-Based method for fuzzy system construction. IEEE Trans Fuzzy Syst 2015; 23(3): 627-43.
Jiang M, Zhang S, Li H, Metaxas DN. Computer-aided diagnosis of mammographic masses using scalable image retrieval. IEEE Trans Biomed Eng 2015; 62(2): 783-92.
[http://dx.doi.org/10.1109/TBME.2014.2365494] [PMID: 25361497]
Quellec G, Lamard M, Cozic M, Coatrieux G, Cazuguel G. Multiple-instance learning for anomaly detection in digital mammography. IEEE Trans Med Imaging 2016; 35(7): 1604-14.
[http://dx.doi.org/10.1109/TMI.2016.2521442] [PMID: 26829783]
Korkmaz AS, Korkmaz FM. A new method based on cancer detection in mammogram textures by finding feature weights and using Kullback–Leibler measure with kernel estimation. Elsevier 2015; 126(20): 2576-83.
Papageorgiou EI, Jayashree S, Akila K, Ni-kolaos P. A risk management model for familial breast cancer: A new application using Fuzzy Cognitive Map method. Comput Methods Programs Biomed 2015; 122(2): 123-35.
Pak F, Hamidreza RK, Alikhassi A. Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution. Comput Methods Programs Biomed 2015; 122(2): 89-107.
Sheta A, Braik MS, Aljahdali S. Genetic Algorithms: A Tool for Image Segmentation. In: International Conference on Multimedia Computing and Systems. Tangier, Morocco 2012; pp. 84-90.
How to Evaluate a Classification Machine Learning Model Available From:. https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5
Ghosh P. Medical image segmentation using a genetic algorithm thesis report. Portland State University 2010.
Bhattacharya M, Sharma N, Goyal V, Bhatia S. A study on genetic algorithm based hybrid soft computing model for benignancy/malignancy detection of masses using digital mammogram. Int J Comput Intell Appl 2011; 10: 141-65.
Mohanta RK, Sethi B. Review of genetic algorithm application for image segmentation. Int J Comp Technol Appl 2012; 3(1): 720-3.
Gorunescu F, Belciug S. Evolutionary strategy to develop learning-based decision systems. Application to breast cancer and liver fibrosis stadialization. J Biomed Inform 2014; 49(1): 112-8.
[http://dx.doi.org/10.1016/j.jbi.2014.02.001] [PMID: 24518558]
Verma B, Zhang P. A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms. Appl Soft Comput 2007; 7(2): 612-25.
Thawkar S, Ranjana I. Classification of masses in digital mammograms using Bioge-ography-based optimization technique. J King Saud Uni Comp Inform Sci 2018; 2018: 1-9.
Sampaioa WB, Silvaa AC, Paiva AC, Gattass M. Detection of masses in mammograms with adaption to breast density using a genetic algorithm, phylogenetic trees, LBP and SVM. Expert Syst Appl 2015; 42(22): 8911-28.
Cao C, Liu F, Tan H, et al. Deep learning and its applications in biomedicine. Genomics Proteomics Bioinformatics 2018; 16(1): 17-32.
[http://dx.doi.org/10.1016/j.gpb.2017.07.003] [PMID: 29522900]
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42(1): 60-88.
[http://dx.doi.org/10.1016/j.media.2017.07.005] [PMID: 28778026]
Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS. Deep learning for visual understanding: a review. Neurocomputing 2016; 187(1): 27-48.
Chougrad H, Zouaki H, Alheyane O. Deep Convolutional Neural Networks for breast cancer screening. Comput Methods Programs Biomed 2018; 157(1): 19-30.
[http://dx.doi.org/10.1016/j.cmpb.2018.01.011] [PMID: 29477427]
Dhungel N, Carneiro G, Andrew BP. The automated learning of deep features for breast mass classification from mammograms. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Athens: Greece 2016; pp. 106-4.
Dhungel N, Carneiro G, Bradley AP. Fully automated classification of mammograms using deep residual neural networks. Supported by the Australian Research Council Discovery Project 2018.
Hamidinekoo A, Denton E, Rampun A. Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 2018; 47: 45-67.
Mohamed AA, Berg WA, Peng H, Luo Y, Jankowitz RC, Wu S. A deep learning method for classifying mammographic breast density categories. Med Phys 2018; 45(1): 314-21.
[http://dx.doi.org/10.1002/mp.12683] [PMID: 29159811]
Dubrovina A, Kisilev P, Ginsburg B, Hashoul S, Kimmel R. Computational mammography using deep neural networks. Comput Methods Biomech Biomed Eng Imaging Vis 2018; 6(3): 243-7.
Ribli D, Horváth A, Unger Z, Pollner P, Csabai I. Detecting and classifying lesions in mammograms with Deep Learning. Sci Rep 2018; 8(1): 4165.
[http://dx.doi.org/10.1038/s41598-018-22437-z] [PMID: 29545529]
Geras KJ, Wolfson S, Shen Y, et al. High-resolution breast cancer screening with multi-view deep convolutional neural networks. IEEE 2018; 2018: 1-9.
Zhang J, Silber JI, Mazurowski MA. Modeling false positive error making patterns in radiology trainees for improved mammography education. J Biomed Inform 2015; 54(1): 50-7.
[http://dx.doi.org/10.1016/j.jbi.2015.01.007] [PMID: 25640462]
Miranda GH, Felipe JC. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput Biol Med 2015; 64(1): 334-46.
[http://dx.doi.org/10.1016/j.compbiomed.2014.10.006] [PMID: 25453323]
Karthikeyan G. One-class classification of mammograms using trace transform functionals. IEEE Trans Instrum Meas 2014; 63(2): 304-11.
Yu. Shyr-Shen. Automatic detection of abnormal mammograms in mammographic images. Expert Syst Appl 2015; 42(1): 3048-55.
Mammogram and image analysis Available from:. http://www.mammoimage.org/databases/

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2020
Page: [639 - 652]
Pages: 14
DOI: 10.2174/1573405615666190903141554
Price: $65

Article Metrics

PDF: 15