Abstract
Breast cancer is a very common and life-threatening disease in women worldwide. The number of breast cancer cases is increasing with time. Prevention of this disease is very challenging and still remains a question at large, but if detected in advance, the survival rate can be increased. The advances in deep learning have demonstrated a lot of changes in the development of Computer-Aided Diagnosis (CAD) of breast cancer. With the noteworthy progress of the new development of artificial intelligence which is deep neural networks, the diagnostic potentialities of deep learning methods are closely approaching the expertise of a human. Although deep learning has substantial improvements and advancements, especially Convolutional Neural Networks (CNN), there are still some challenges that are required to be addressed to build an effective CAD system that can serve as a “second opinion” tool for practitioners. A comprehensive review of clinical aspects of breast cancer like risk factors, breast abnormalities, and BIRADS (Breast Imaging Reporting and Data System) is presented in the paper. This paper also presents CAD systems that are recently developed for breast cancer segmentation, detection, and classification. An overview of mammography datasets used in literature and challenges in applying CNN for medical images are also discussed in the paper.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
A. Jalalian, S. Mashohor, R. Mahmud, B. Karasfi, M.I.B. Saripan, A.R.B. Ramli, Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. EXCLI J. 16, 113 (2017)
H. Greenspan, B. Van Ginneken, R.M. Summers, Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging 35(5), 1153–1159 (2016)
Alarming facts about breast cancer in India [Online]. Available: https://www.oncostem.com/blog/alarming-facts-about-breast-cancer-in-india
Breast cancer risk factors. [Online]. Available: https://www.breastcancer.org/symptoms/understandbc/risk/factors
B. Hela, M. Hela, H. Kamel, B. Sana, M. Najla, Breast cancer detection: A review on mammograms analysis techniques, in 10th International Multi Conferences on Systems, Signals & Devices 2013 (SSD13) (IEEE, 2013), pp. 1–6
S. Gaur, V. Dialani, P.J. Slanetz, R.L. Eisenberg, Architectural distortion of the breast. Am. J. Roentgenol. 201(5), 662–670 (2013)
J.P. Suckling, The mammographic image analysis society digital mammogram database. Digital Mammo 375–386 (1994)
J. Suckling, J. Parker, D. Dance, S. Astley, I. Hutt, C. Boggis, I. Ricketts, E. Stamatakis, N. Cerneaz, S. Kok et al., Mammographic Image Analysis Society (MIAS) Database v1. 21 (2015)
The digital database for screening mammography, 2001. [Online]. Available: http://www.eng.usf.edu/cvprg/Mammography/Database.html
S.J. Magny, R. Shikhman, A.L. Keppke, Breast, Imaging, Reporting and Data System (bi-rads). StatPearls [Internet] (2020)
L. Liberman, J.H. Menell, Breast imaging reporting and data system (bi-rads). Radiologic Clinics 40(3), 409–430 (2002)
K. Doi, Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput. Med. Imaging Graphics 31(4–5), 198–211 (2007)
J.R. Burt, N. Torosdagli, N. Khosravan, H. RaviPrakash, A. Mortazi, F. Tissavi- rasingham, S. Hussein, U. Bagci, Deep learning beyond cats and dogs: Recent advances in diagnosing breast cancer with deep neural networks. Br. J. Radiol. 91(1089), 20170545 (2018)
R. Pillai, P. Oza, P. Sharma, Review of machine learning techniques in health care, in Proceedings of ICRIC 2019. Lecture Notes in Electrical Engineering, vol. 597 (Springer, 2020), pp. 103–111
Y. Bengio, Learning Deep Architectures for AI. Now Publishers Inc, (2009)
A. Hamidinekoo, E. Denton, A. Rampun, K. Honnor, R. Zwiggelaar, Deep learning in mammography and breast histology, an overview and future trends. Med. Image Anal. 47, 45–67 (2018)
A.S. Lundervold, A. Lundervold, An overview of deep learning in medical imaging focusing on MRI. Zeitschrift four Medizinische Physik 29(2), 102–127 (2019)
Y. LeCun, Y. Bengio, G. Hinton, Deep learning, nature 521(7553), 436–444 (2015)
G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J.A. Van Der Laak, B. Van Ginneken, C.I. Sanchez, A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems (2012), pp. 1097–1105
K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556 (2014)
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012) pp. 1–9
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778
N.S. Ismail, C. Sovuthy, Breast cancer detection based on deep learning technique, in 2019 International UNIMAS STEM 12th Engineering Conference (EnCon) (2019), pp. 89–92
M. Yemini, Y. Zigel, D. Lederman, Detecting masses in mammograms using convolutional neural networks and transfer learning, in 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE) (IEEE, 2018), pp. 1–4
H.-C. Lu, E.-W. Loh, S.-C. Huang, The classification of mammogram using convolutional neural network with specific image preprocessing for breast cancer detection, in 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) (IEEE, 2019), pp. 9–12
M.A. Al-Masni, M.A. Al-Antari, J. Park, G. Gi, T.-Y. Kim, P. Rivera, E. Valarezo, S.-M. Han, T.-S. Kim, Detection and classification of the breast abnormalities in digital mammograms via regional convolutional neural network, in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2017), pp. 1230–1233
X. Zhao, X. Wang, H. Wang, Classification of benign and malignant breast mass in digital mammograms with convolutional neural networks, in Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine (2018), pp. 47–50
F. Jiang, H. Liu, S. Yu, Y. Xie, Breast mass lesion classification in mammograms by transfer learning, in Proceedings of the 5th International Conference on Bioinformatics and Computational Biology (2017), pp. 59–62
R. Platania, S. Shams, S. Yang, J. Zhang, K. Lee, S.-J. Park, Automated breast cancer diagnosis using deep learning and region of interest detection (bc-droid), in Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (2017), pp. 536–543
B. Li, Y. Ge, Y. Zhao, E. Guan, W. Yan, Benign and malignant mammographic image classification based on convolutional neural networks, in Proceedings of the 2018 10th International Conference on Machine Learning and Computing (2018), pp. 247–251
Q. Zeng, H. Jiang, L. Ma, Learning multi-level features for breast mass detection, in Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine (2018), pp. 16–20
N. Dhungel, G. Carneiro, A.P. Bradley, A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med. Image Anal. 37, 114–128 (2017)
W. Zhu, X. Xiang, T.D. Tran, G. D. Hager, X. Xie, Adversarial deep structured nets for mass segmentation from mammograms, in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (IEEE, 2018), pp. 847–850
D. Ribli, A. Horvath, Z. Unger, P. Pollner, I. Csabai, Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8(1), 1–7 (2018)
L. Shen, L.R. Margolies, J.H. Rothstein, E. Fluder, R.B. McBride, W. Sieh, Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography. arXiv preprint arXiv:1708.09427 (2017)
R.S. Lee, F. Gimenez, A. Hoogi, D. Rubin, Curated breast imaging subset of DDSM, Cancer Imaging Archive 8 (2016)
R.S. Lee, F. Gimenez, A. Hoogi, K.K. Miyake, M. Gorovoy, D.L. Rubin, A curated mammography dataset for use in computer-aided detection and diagnosis research. Sci. Data 4, 170177 (2017)
K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, S. Moore, S. Phillips, D. Maffitt, M. Pringle et al., The cancer imaging archive (TCIA): Maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)
C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M.J. Cardoso, J.S. Cardoso, Inbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)
O. Parita, et al., A bottom-up review of image analysis methods for suspicious region detection in mammograms. J. Imaging 7(9), 190 (2021)
G. Choy, O. Khalilzadeh, M. Michalski, S. Do, A.E. Samir, O.S. Pianykh, J.R. Geis, P.V. Pandharipande, J.A. Brink, K.J. Dreyer, Current applications and future impact of machine learning in radiology. Radiology 288(2), 318–328 (2018)
Madan, D. Dindi, Up to Speed on Deep Learning in Medical Imaging (2016) [Online]. Available: https://medium.com/the-mission/up-to-speed-on-deep-learning-in-medical-imaging-7ff1e91f6d71
N. Tajbakhsh, J.Y. Shin, S.R. Gurudu, R.T. Hurst, C.B. Kendall, M.B. Gotway, J. Liang, Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299–1312 (2016)
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Oza, P., Sharma, P., Patel, S. (2022). A Drive Through Computer-Aided Diagnosis of Breast Cancer: A Comprehensive Study of Clinical and Technical Aspects. In: Singh, P.K., Singh, Y., Kolekar, M.H., Kar, A.K., Gonçalves, P.J.S. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 832. Springer, Singapore. https://doi.org/10.1007/978-981-16-8248-3_19
Download citation
DOI: https://doi.org/10.1007/978-981-16-8248-3_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8247-6
Online ISBN: 978-981-16-8248-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)