Generic placeholder image

Current Chinese Science


ISSN (Print): 2210-2981
ISSN (Online): 2210-2914

Review Article Section: Artificial Intelligence and Robotics

Breast Cancer Segmentation in Mammogram Using Artificial Intelligence and Image Processing: A Systematic Review

Author(s): Wajeeha Ansar and Basit Raza*

Volume 3, Issue 1, 2023

Published on: 22 September, 2022

Page: [3 - 22] Pages: 20

DOI: 10.2174/2210298102666220406121814

Price: $65


Background: Breast cancer is the second leading cause of death in females worldwide. Mammograms are useful in early cancer diagnosis as well when the patient can sense symptoms or they become observable. Inspection of mammograms in search of breast tumors is a difficult task that radiologists must carry out frequently.

Objective: This paper provides a summary of possible strategies used in automated systems for a mammogram, especially focusing on segmentation techniques used for cancer localization in mammograms.

Methods: This article is intended to present a brief overview for nonexperts and beginners in this field. It starts with an overview of the mammograms, public and private available datasets, image processing techniques used for a mammogram and cancer classification followed by cancer segmentation using the machine and deep learning techniques.

Conclusion: The approaches used in these stages are summarized, and their advantages and disadvantages with possible future research directions are discussed. In the future, we will train a model of medical images that can be used for transfer learning in mammograms.

Keywords: Breast cancer, mammogram, cancer segmentation, deep learning, image processing, masses, benign.

DeSantis, C.E.; Ma, J.; Gaudet, M.M.; Newman, L.A.; Miller, K.D.; Goding Sauer, A.; Jemal, A.; Siegel, R.L. Breast cancer statistics, 2019. CA Cancer J. Clin., 2019, 69(6), 438-451.
[] [PMID: 31577379]
Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin., 2019, 69(1), 7-34.
[] [PMID: 30620402]
World Health Organization (WHO) 2019 Breast Cancer, 2019. Available from:
Lundberg, A. Transcriptional gene signatures: Passing the restriction point for routine clinical implementation. Phd Thesis: Uni of California, SanFrancisco. 2019.
Kerlikowske, K.; Carney, P.A.; Geller, B.; Mandelson, M.T.; Taplin, S.H.; Malvin, K.; Ernster, V.; Urban, N.; Cutter, G.; Rosenberg, R.; Ballard-Barbash, R. Performance of screening mammography among women with and without a first-degree relative with breast cancer. Ann. Intern. Med., 2000, 133(11), 855-863.
[] [PMID: 11103055]
Bird, R.E.; Wallace, T.W.; Yankaskas, B.C. Analysis of cancers missed at screening mammography. Radiology, 1992, 184(3), 613-617.
[] [PMID: 1509041]
Ponraj, D.N.; Jenifer, M.E.; Poongodi, P.; Manoharan, J.S. A survey on the preprocessing techniques of mammogram for the detection of breast cancer. J. Emerg.Trends Comput. Inform. Sci., 2011, 2(12), 656-664.
Oliver, A.; Freixenet, J.; Martí, J.; Pérez, E.; Pont, J.; Denton, E.R.; Zwiggelaar, R. A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal., 2010, 14(2), 87-110.
[] [PMID: 20071209]
Jalalian, A.; Mashohor, S.B.; Mahmud, H.R.; Saripan, M.I.B.; Ramli, A.R.B.; Karasfi, B. Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: A review. Clin. Imag, 2013, 37(3), 420-426.
[] [PMID: 23153689]
Gardezi, S.J.S.; Elazab, A.; Lei, B.; Wang, T. Breast cancer detection and diagnosis using mammographic data: Systematic review. J. Med. Internet Res., 2019, 21(7), e14464.
[] [PMID: 31350843]
Ramani, R.; Suthanthiravanitha, S.; Valarmathy, S. A survey of current image segmentation techniques for detection of breast cancer. Int. J. Eng. Res. Appl., 2012, 2(5), 1124-1129. [IJERA]
Ramani, R.; Valarmathy, S.; Vanitha, N.S. Breast cancer detection in mammograms based on clustering techniques-a survey. Int. J. Comput. Appl., 2013, 62(11), 17-21.
Heath, M.; Bowyer, K.; Kopans, D.; Kegelmeyer, P.; Moore, R.; Chang, K. Current status of the digital database for screening mammography. In: Digital mammography; Springer: Dordrecht, 1998; pp. 457-460.
Moreira, I.C.; Amaral, I.; Domingues, I.; Cardoso, A.; Cardoso, M.J.; Cardoso, J.S. INbreast: Toward a full-field digital mammographic database. Acad. Radiol., 2012, 19(2), 236-248.
[] [PMID: 22078258]
SUCKLING, J P. The mammographic image analysis society digital mammogram database. Digital Mammo 1994, 375-386.
Lopez, M.G.; Posada, N.; Moura, D.C.; Pollán, R.R.; Valiente, J.M.F.; Ortega, C.S. BCDR: A breast cancer digital repository. In: 15th International conference on experimental mechanics; , 2012; pp. 1065-1066.
Matheus, B.R.N.; Schiabel, H. Online mammographic images database for development and comparison of CAD schemes. J. Digit. Imaging, 2011, 24(3), 500-506.
[] [PMID: 20480383]
Lee, R.S.; Gimenez, F.; Hoogi, A.; Miyake, K.K.; Gorovoy, M.; Rubin, D.L. A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data, 2017, 4(1), 170177.
[] [PMID: 29257132]
Marzulli, V. The CALMA project: Computer assisted library for mammography. Neural Nets WIRN VIETRI-98, 1999, 230-235.
Augusto, GB Multiple Kernel Learning for Breast Cancer Classification, 2014.
Ashby, A.E.; Hernandez, J.M.; Logan, C.M.; Mascio, L.N.; Frankel, S.; Kegelmeyer, W.P. UCSF/LLNL high resolution digital mammogram library. In: 17th International Conference of the Engineering in Medicine and Biology Society; , 1995; 1, pp. 539-540.
Gardner, W.D. Breast Cancer database provides faster access to patient record. Grid technology is at the heart of this massive database that holds over a million mammography images. Information Week, 2005.
Halling-Brown, M.D.; Warren, L.M.; Ward, D.; Lewis, E.; Mackenzie, A.; Wallis, M.G. OPTIMAM Mammography image database: A large scale resource of mammography images and clinical data. Radiol: Artf. Intell, 2020, 3(1)
Dehghani, S.; Dezfooli, M.A. A method for improve preprocessing images mammography. Int. J. Inf. Educ. Technol., 2011, 1(1), 90-93.
Elmoufidi, A. Pre-processing algorithms on digital X-ray mammograms. IEEE International Smart Cities Conference (ISC2); IEEE, 2019.
Lu, H.C.; Loh, E.W.; Huang, S.C. The classification of mammogram using convolutional neural network with specific image preprocessing for breast cancer detection. 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) IEEE, 2019.
Maitra, I.K.; Nag, S.; Bandyopadhyay, S.K. Technique for preprocessing of digital mammogram. Comput. Methods Prog. Biomed., 2012, 107(2), 175-188.
[] [PMID: 21669471]
Sundaram, K.M.; Sasikala, D.; Rani, P.A. A study on preprocessing a mammogram image using adaptive median filter. Int. J. Innov. Res. Sci. Eng. Technol., 2014, 3(3), 10333-10337.
Tripathy, S.; Swarnkar, T. Unified preprocessing and enhancement technique for mammogram images. Procedia Comput. Sci., 2020, 167, 285-292.
Ali, M.J.; Raza, B.; Shahid, A.R.; Mahmood, F.; Yousuf, M.A.; Dar, A.H.; Iqbal, U. Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network. Int. J. Imaging Syst. Technol., 2020, 30(4), 1108-1118.
Moghbel, M.; Ooi, C.Y.; Ismail, N.; Hau, Y.W.; Memari, N. A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography. Artif. Intell. Rev., 2019, 1-46.
Rahimeto, S.; Debelee, T.G.; Yohannes, D.; Schwenker, F. Automatic pectoral muscle removal in mammograms. Evol. Syst., 2019, 1-8.
Rahmatika, A.; Handayani, A.; Setiawan, A.W. Automated segmentation of breast tissue and pectoral muscle in digital mammography. In: International Conference of Artificial Intelligence and Information Technology (ICAIIT); , 2019; pp. 397-401.
Shinde, V.; Rao, B.T. Novel approach to segment the pectoral muscle in the mammograms. Cognitive Inform. Soft Comput., 2019, 227-237.
Wang, K.; Khan, N.; Chan, A.; Dunne, J.; Highnam, R. Deep learning for breast region and pectoral muscle segmentation in digital mammography. Pacific-Rim Symposium on Image and Video Technology, 2019.
Bhateja, V.; Misra, M.; Urooj, S. Mammogram Enhancement and Associated Challenges. In: Non-Linear Filters for Mammogram Enhancement; Springer: Singapore, 2020; pp. 31-34.
Bhateja, V.; Misra, M.; Urooj, S. Region-based and feature based mammogram enhancement techniques. In: Non-Linear Filters for Mammogram Enhancement; Springer: Singapore, 2020; pp. 47-54.
Rojas Domínguez, A.; Nandi, A.K. Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection. Comput. Med. Imaging Graph., 2008, 32(4), 304-315.
[] [PMID: 18358699]
Kashyap, K.L.; Bajpai, M.K.; Khanna, P.; Giakos, G. Mesh-free based variational level set evolution for breast region segmentation and abnormality detection using mammograms. Int. J. Numer. Methods Biomed. Eng., 2018, 34(1), e2907.
[] [PMID: 28603939]
Sampat, M.P.; Bovik, A.C. Detection of spiculated lesions in mammograms. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE, 2003.
Dabour, W. Improved wavelet based thresholding for contrast enhancement of digital mammograms. In: 2008 International Conference on Computer Science and Software Engineering; , 2008; pp. 948-951.
Gagnon, L.; Lina, J.; Goulard, B. Sharpening enhancement of digitized mammograms with complex symmetric Daubechies wavelets. In: Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society; , 1995; 1, pp. 543-544.
Chang, C.M. Laine, Enhancement of mammograms from oriented information. A. In: Proceedings of International Conference on Image Proc., 1997, 3, 524-527.
Scharcanski, J.; Jung, C.R. Denoising and enhancing digital mammographic images for visual screening. Comput. Med. Imaging Graph., 2006, 30(4), 243-254.
[] [PMID: 16839742]
Sakellaropoulos, P.; Costaridou, L.; Panayiotakis, G. A wavelet-based spatially adaptive method for mammographic contrast enhancement. Phys. Med. Biol., 2003, 48(6), 787-803.
[] [PMID: 12699195]
Gorgel, P.; Sertbas, A.; Ucan, O.N. A wavelet-based mammographic image denoising and enhancement with homomorphic filtering. J. Med. Syst., 2010, 34(6), 993-1002.
[] [PMID: 20703608]
Kumar, S.; Chandra, M. Detection of microcalcification using the wavelet based adaptive sigmoid function and neural network. JIPS, 2017, 13(4), 703-715.
Vikhe, P.; Thool, V. A wavelet and adaptive threshold-based contrast enhancement of masses in mammograms for visual screening. Int. J. Biomed. Eng. Technol., 2019, 30(1), 31-53.
Bovis, K.; Singh, S. Detection of masses in mammograms using texture features. In: Proceedings 15th International Conference on Pattern Recognition ICPR-2000; , 2000; 2, pp. 267-270.
Yu, Z.; Bajaj, C. A fast and adaptive method for image contrast enhancement. In: International Conference on Image Processing IEEE; , 2004; 1, pp. 1001-1004.
Hemminger, B.M.; Zong, S.; Muller, K.E.; Coffey, C.S.; DeLuca, M.C.; Johnston, R.E.; Pisano, E.D. Improving the detection of simulated masses in mammograms through two different image-processing techniques. Acad. Radiol., 2001, 8(9), 845-855.
[] [PMID: 11724039]
Kom, G.; Tiedeu, A.; Kom, M. Automated detection of masses in mammograms by local adaptive thresholding. Comput. Biol. Med., 2007, 37(1), 37-48.
[] [PMID: 16487954]
Dabass, J.; Arora, S.; Vig, R.; Hanmandlu, M. Mammogram image enhancement using entropy and CLAHE based intuitionistic fuzzy method. 6th International Conference on Signal Processing and Integrated Networks IEEE, 2019, 24-29.
Muneeswaran, V.; Rajasekaran, M.P. Local contrast regularized contrast limited adaptive histogram equalization using tree seed algorithm-An aid for mammogram images enhancement. In: Smart Intelligent Computing and Applications; Springer: Singapore, 2019; pp. 693-701.
Cheng, H.D.; Xu, H. A novel fuzzy logic approach to mammogram contrast enhancement. Inf. Sci., 2002, 148(1-4), 167-184.
Jiang, J.; Yao, B.; Wason, A.M. Integration of fuzzy logic and structure tensor towards mammogram contrast enhancement. Comput. Med. Imaging Graph., 2005, 29(1), 83-90.
[] [PMID: 15710543]
Chaira, T. Intuitionistic fuzzy approach for enhancement of low contrast mammogram images. Int. J. Imaging Syst. Technol., 2020, 30(4), 1162-1172.
Kalra, P.K.; Kumar, N. An automatic method to enhance microcalcifications using Normalized Tsallis entropy. Signal Processing, 2010, 90(3), 952-958.
Chan, N.H.; Hasikin, K.; Kadri, N.A. An improved enhancement technique for mammogram image analysis: A fuzzy rule-based approach of contrast enhancement. In: 15th International Colloquium on Signal Processing & Its Applications; IEEE , 2019; pp. 202-206.
Mohan, M.; Nair, L.S. Fuzzy c-means segmentation on enhanced mammograms using clahe and fourth order complex diffusion. In: 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC); , 2020; pp. 647-651.
Ojala, T.; Näppi, J.; Nevalainen, O. Accurate segmentation of the breast region from digitized mammograms. Comput. Med. Imaging Graph., 2001, 25(1), 47-59.
[] [PMID: 11120407]
Chen, Z.; Zwiggelaar, R. Segmentation of the breast region with pectoral muscle removal in mammograms. Medical Image Understanding and Analysis; MIUA, 2010, pp. 71-76.
Wang, K.; Qin, H.; Fisher, P.R.; Zhao, W. Automatic Registration of Mammograms using Texture-based Anisotropic Features. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro; , 2006; pp. 64-867.
Shahedi, M.B.; Amirfattahi, R.; Azar, F.T.; Sadri, S. Accurate breast region detection in digital mammograms using a local adaptive thresholding method. In: 8th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS’07); , 2007; p. 26.
Xu, S.; Liu, H.; Xu, X.; Song, E.; Zeng, J. Bilateral asymmetry detection in mammograms using non-rigid registraion and pseudo-color coding. In: 2010 International Conference on Electrical and Control Engineering; , 2010; pp. 544-547.
Singh, V.K.; Rashwan, H.A.; Romani, S.; Akram, F.; Pandey, N.; Sarker, M.M.K.; Saleh, A.; Arenas, M.; Arquez, M.; Puig, D.; Torrents-Barrena, J. Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst. Appl., 2020, 139, 112855.
Altrichter, M.; Ludányi, Z.; Horváth, G. Joint analysis of multiple mammographic views in CAD systems for breast cancer detection. Scandinavian conference on image analysis;; Springer: Berlin, 2005.
Abdel-Nasser, M.; Moreno, A.; Abdelwahab, M.A.; Saleh, A.; Abdulwahab, S.; Singh, V.K. Matching tumour candidate points in multiple mammographic views for breast cancer detection. In: International Conference on Innovative Trends in Computer Engineering (ITCE); , 2019; pp. 202-207.
Sasikala, S.; Ezhilarasi, M.; Kumar, S.A. Detection of breast cancer using fusion of MLO and CC view features through a hybrid technique based on binary firefly algorithm and optimum-path forest classifier. Applied Nature-Inspired Computing: Algorithms and Case Studies; Springer: Singapore, 2020, pp. 23-40.
Sasikala, S.; Bharathi, M.; Ezhilarasi, M.; Arunkumar, S. Breast cancer detection based on medio-lateral obliqueview and craniocaudal view mammograms: An overview. IEEE 10th International Conference on Awareness Science and Technology; IEEE, 2019.
Loizidou, K.; Skouroumouni, G.; Nikolaou, C.; Pitris, C. An automated breast micro-calcification detection and classification technique using temporal subtraction of mammograms. IEEE Access, 2020, 8, 52785-52795.
Wirth, M.A.; Narhan, J.; Gray, D.W. Nonrigid mammogram registration using mutual information. In: Medical Imaging 2002: Image Processing; International Society for Optics and Photonics, 2002; pp. 562-573.
Timp, S.; van Engeland, S.; Karssemeijer, N. A regional registration method to find corresponding mass lesions in temporal mammogram pairs. Med. Phys., 2005, 32(8), 2629-2638.
[] [PMID: 16193793]
Zhang, L.; Li, Y.; Chen, H.; Cheng, L. Mammographic mass detection by bilateral analysis based on convolution neural network. In: 2019 IEEE International Conference on Image Processing (ICIP); , 2019; pp. 784-788.
Li, Y.; Zhang, L.; Chen, H.; Cheng, L. Mass detection in mammograms by bilateral analysis using convolution neural network. Comput. Methods Programs Biomed., 2020, 195, 105518.
[] [PMID: 32480189]
Jouirou, A.; Baâzaoui, A.; Barhoumi, W. Multi-view information fusion in mammograms: A comprehensive overview. Inf. Fusion, 2019, 52, 308-321.
Suhail, Z.; Zwiggelaar, R. Histogram-based approach for mass segmentation in mammograms. In: 15th International Workshop on Breast Imaging (IWBI2020); , 2020; p. 1151325.
Li, L.; Qian, W.; Clarke, L.P.; Clark, R.A.; Thomas, J.A. Improving mass detection by adaptive and multiscale processing in digitized mammograms. In: Medical Imaging 1999: Image Processing; International Society for Optics and Photonics, 1999; p. 3661.
Li, H.D.; Kallergi, M.; Clarke, L.P.; Jain, V.K.; Clark, R.A. Markov random field for tumor detection in digital mammography. IEEE Trans. Med. Imaging, 1995, 14(3), 565-576.
[] [PMID: 18215861]
Zhang, Y.; Tomuro, N.; Furst, J.; Raicu, D.S. Image enhancement and edge-based mass segmentation in mammogram. In: Medical Imaging 2010: Image Processing; International Society for Optics and Photonics, 2010; p. 7623.
Abbas, Q.; Celebi, M.E.; García, I.F. Breast mass segmentation using region-based and edge-based methods in a 4-stage multiscale system. Biomed. Signal Process. Control, 2013, 8(2), 204-214.
Lee, Y.J.; Park, J.M.; Park, H.W. Mammographic mass detection by adaptive thresholding and region growing. Int. J. Imaging Syst. Technol., 2000, 11(5), 340-346.
Bharadwaj, A.S.; Celenk, M. Detection of microcalcification with top-hat transform and the Gibbs random fields. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society., IEEE 2015. 2015, 6382-5.
Derin, H.; Elliott, H. Modeling and segmentation of noisy and textured images using gibbs random fields. IEEE Trans. Pattern Anal. Mach. Intell., 1987, 9(1), 39-55.
[] [PMID: 21869376]
Cui, W.J.; Wang, C.; Jia, L.; Ren, S.; Duan, S.F.; Cui, C.; Chen, X.; Wang, Z.Q. Differentiation between G1 and G2/G3 phyllodes tumors of breast using mammography and mammographic texture analysis. Front. Oncol., 2019, 9, 433.
[] [PMID: 31192133]
D’Elia, C.; Marrocco, C.; Molinara, M.; Poggi, G.; Scarpa, G.; Tortorella, F. Detection of microcalcifications clusters in mammograms through TS-MRF segmentation and SVM-based classification. Proceedings of the 17th International Conference on Pattern Recognition IEEE, 2004.
Wei, J.; Sahiner, B.; Hadjiiski, L.M.; Chan, H.P.; Petrick, N.; Helvie, M.A.; Roubidoux, M.A.; Ge, J.; Zhou, C. Computer-aided detection of breast masses on full field digital mammograms. Med. Phys., 2005, 32(9), 2827-2838.
[] [PMID: 16266097]
Samulski, M.; Karssemeijer, N. Optimizing Case-based detection performance in a multiview CAD system for mammography. IEEE Trans. Med. Imaging, 2011, 30(4), 1001-1009.
[] [PMID: 21233045]
Identifying masses in mammograms using template matching. Lochanambal, K.; Karnan, M. Sivakumar, R., Eds.; 2010 Second International Conference on Communication Software and Networks, 2010.
Song, E.; Xu, S.; Xu, X.; Zeng, J.; Lan, Y.; Zhang, S.; Hung, C.C. Hybrid segmentation of mass in mammograms using template matching and dynamic programming. Acad. Radiol., 2010, 17(11), 1414-1424.
[] [PMID: 20817575]
Liu, S.; Babbs, C.F.; Delp, E.J. Multiresolution detection of spiculated lesions in digital mammograms. IEEE Trans. Image Process., 2001, 10(6), 874-884.
Zheng, L.; Chan, A.K. An artificial intelligent algorithm for tumor detection in screening mammogram. IEEE Trans. Med. Imaging, 2001, 20(7), 559-567.
[] [PMID: 11465463]
Campanini, R.; Dongiovanni, D.; Iampieri, E.; Lanconelli, N.; Masotti, M.; Palermo, G.; Riccardi, A.; Roffilli, M. A novel featureless approach to mass detection in digital mammograms based on support vector machines. Phys. Med. Biol., 2004, 49(6), 961-975.
[] [PMID: 15104319]
Muralidhar, G.S.; Bovik, A.C.; Giese, J.D.; Sampat, M.P.; Whitman, G.J.; Haygood, T.M.; Stephens, T.W.; Markey, M.K. Snakules: A model-based active contour algorithm for the annotation of spicules on mammography. IEEE Trans. Med. Imaging, 2010, 29(10), 1768-1780.
[] [PMID: 20529728]
de Oliveira Martins, L.; Junior, G.B.; Silva, A.C.; de Paiva, A.C.; Gattass, M. Detection of masses in digital mammograms using K-means and support vector machine. ELCVIA Electron. Lett. Comput. Vis. Image Anal., 2009, 8(2), 39-50.
Mousa, R.; Munib, Q.; Moussa, A. Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural. Expert Syst. Appl., 2005, 28(4), 713-723.
Hassanien, A. Fuzzy rough sets hybrid scheme for breast cancer detection. Image Vis. Comput., 2007, 25(2), 172-183.
Kamil, M.Y.; Salih, A.M. Mammography Images Segmentation via Fuzzy C-mean and K-mean. Inter. J. Intell. Eng. Syst., 2019, 12(1), 22-29.
Li, F.; Shang, C.; Li, Y.; Shen, Q. Interpretable mammographic mass classification with fuzzy interpolative reasoning. Knowl. Base. Syst., 2020, 191, 105279.
Kamil, M.Y.; Salih, A.M. Breast tumor detection via fuzzy morphological operations. Int. J. Adv. Pervasive Ubiquitous Comput., 2019, 11(1), 33-44.
Le, T.L.T.; Thome, N.; Bernard, S.; Bismuth, V.; Patoureaux, F. Multitask classification and segmentation for cancer diagnosis in mammography., 2019.
Min, H.; Wilson, D.; Huang, Y.; Liu, S.; Crozier, S.; Bradley, A.P. Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN. In: IEEE 17th International Symposium on Biomedical Imaging (ISBI) IEEE; , 2020; pp. 1111-1115.
Wang, R.; Ma, Y.; Sun, W.; Guo, Y.; Wang, W.; Qi, Y.; Gong, X. Multi-level nested pyramid network for mass segmentation in mammograms. Neurocomputing, 2019, 363, 313-320.
Li, S.; Dong, M.; Du, G.; Mu, X. Attention dense-u-net for automatic breast mass segmentation in digital mammogram. IEEE Access, 2019, 7, 59037-59047.
Cao, Z.; Yang, Z.; Zhuo, X.; Lin, R.S.; Wu, S.; Huang, L. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, 2019, 362-370.
Sun, H.; Li, C.; Liu, B.; Liu, Z.; Wang, M.; Zheng, H.; Dagan Feng, D. D.; Wang, S. AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms. Phys. Med. Biol., 2020, 65(5), 055005.
[] [PMID: 31722327]
Tsochatzidis, L.; Costaridou, L.; Pratikakis, I. Deep learning for breast cancer diagnosis from mammograms-A comparative study. J. Imaging, 2019, 5(3), 37.
[] [PMID: 34460465]
Breast cancer screening using convolutional neural network and follow-up digital mammography. Zheng, Y.; Yang, C.; Merkulov, A., Eds; Computational Imaging, I.I.I., Ed.; International Society for Optics and Photonics, 2018.
Agarwal, R.; Diaz, O.; Lladó, X.; Yap, M.H.; Martí, R. Automatic mass detection in mammograms using deep convolutional neural networks. J. Med. Imaging (Bellingham), 2019, 6(3), 031409.
Lévy, D.; Jain, A. Breast mass classification from mammograms using deep convolutional neural networks; Cornell University, 2016.
Breast mass lesion classification in mammograms by transfer learning. Jiang, F.; Liu, H.; Yu, S.Xie, Y., Eds. Proceedings of the 5th International Conference on Bioinformatics and Computational Biology, 2017.
Arefan, D.; Mohamed, A.A.; Berg, W.A.; Zuley, M.L.; Sumkin, J.H.; Wu, S. Deep learning modeling using normal mammograms for predicting breast cancer risk. Med. Phys., 2020, 47(1), 110-118.
[] [PMID: 31667873]
Sert, E.; Ertekin, S.; Halici, U. Ensemble of convolutional neural networks for classification of breast microcalcification from mammograms. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE; , 2017; pp. 689-692.
Chen, Y.; Zhang, Q.; Wu, Y.; Liu, B.; Wang, M.; Lin, Y. Fine-tuning resnet for breast cancer classification from mammography. In: The International Conference on Healthcare Science and Engineering; , 2018; pp. 83-96.
Wang, R.; Guo, Y.; Wang, W.; Ma, Y. Bi-ResNet: Fully automated classification of unregistered contralateral mammograms. In: International Conference on Artificial Neural Networks; , 2019.
Dhungel, N.; Carneiro, G.; Bradley, A.P. Fully automated classification of mammograms using deep residual neural networks. In IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) IEEE, 2017, vol. 11731
Mohanty, F.; Rup, S.; Dash, B.; Majhi, B.; Swamy, M. An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine. Appl. Soft Comput., 2020, 91, 106266.
Mohanty, F.; Rup, S.; Dash, B.; Majhi, B.; Swamy, M. Mammogram classification using contourlet features with forest optimization-based feature selection approach. Multimedia Tools Appl., 2019, 78(10), 12805-12834.
Soriano, D.; Aguilar, C.; Ramirez-Morales, I.; Tusa, E.; Rivas, W.; Pinta, M. Mammogram classification schemes by using convolutional neural networks. In: International Conference on Technology Trends; , 2017; p. 798.
Zhang, X.; Zhang, Y.; Han, E.Y.; Jacobs, N.; Han, Q.; Wang, X. Whole mammogram image classification with convolutional neural networks. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); , 2017; pp. 700-704.
Khan, H.N.; Shahid, A.R.; Raza, B.; Dar, A.H.; Alquhayz, H. Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access, 2019, 7, 165724-165733.
Huynh, B.Q.; Li, H.; Giger, M.L. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J. Med. Imaging (Bellingham), 2016, 3(3), 034501.
[] [PMID: 27610399]
Samala, R.K.; Chan, H.P.; Hadjiiski, L.M.; Helvie, M.A.; Cha, K.H.; Richter, C.D. Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms. Phys. Med. Biol., 2017, 62(23), 8894-8908.
[] [PMID: 29035873]
Falconí, L.G.; Pérez, M.; Aguilar, W.G. Transfer learning in breast mammogram abnormalities classification with mobilenet and nasnet. In: 2019 International Conference on Systems, Signals and Image Processing (IWSSIP); , 2019; pp. 109-114.
Adedigba, A.P.; Adeshinat, S.A.; Aibinu, A.M. Deep learning-based mammogram classification using small dataset. In: 2019 15th International Conference on Electronics, Computer and Computation (ICECCO); IEEE , 2019; pp. 1-6.
Ansar, W.; Shahid, A.R.; Raza, B.; Dar, A.H. Breast cancer detection and localization using mobilenet based transfer learning for mammograms. In: Intelligent Computing Systems, Third International Symposium, ISICS 2020, Sharjah, United Arab Emirates, pp. , 11-21.
Zhu, W.; Lou, Q.; Vang, Y.S.; Xie, X. Deep multi-instance networks with sparse label assignment for whole mammogram classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention; , 2017; p. 10435.
Jadoon, MM; Zhang, Q.; Haq, IU; Butt, S. Jadoon, A Three-class mammogram classification based on descriptive CNN features. Biomed Res. Int., 2017, 2017, 3640901.
Uppal, MTN Classification of mammograms for breast cancer detection using fusion of discrete cosine transform and discrete wavelet transform features. Biomed. Res., 2016, 27(2)
Performance analysis and detection of micro calcification in digital mammograms using wavelet features. Abirami, C.; Harikumar, R. Chakravarthy, S.S., Eds. 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016.
Lotter, W.; Sorensen, G.; Cox, D. A multi-scale CNN and curriculum learning strategy for mammogram classification. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support; Springer, 2017, pp. 169-177.
Wu, E.; Wu, K.; Cox, D.; Lotter, W. Conditional infilling GANs for data augmentation in mammogram classification. Image Analysis for Moving Organ, Breast, and Thoracic Images; Springer, 2018, pp. 98-106.
Carneiro, G.; Nascimento, J.; Bradley, A.P. Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans. Med. Imaging, 2017, 36(11), 2355-2365.
[] [PMID: 28920897]
Wang, H.; Feng, J.; Zhang, Z.; Su, H.; Cui, L.; He, H.; Liu, L. Breast mass classification via deeply integrating the contextual information from multi-view data. Pattern Recognit., 2018, 80, 42-52.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy