Abstract
Breast cancer is considered one of the primary causes of mortality among women aged 20–59 worldwide. Early detection and treatment can allow patients to have proper treatment and consequently reduce rate of morbidity of breast cancer. Research indicates that most experienced physicians can diagnose cancer with 79% accuracy while 91% correct diagnosis is achieved using machine learning techniques. In this paper, we present the most recent breast cancer detection and classification models that are machine learning based models by analyzing them in the form of comparative study. Also, in this paper, the datasets that are public for use and popular as well are listed in the recent work to facilitate any new experiments and comparisons. The comparative analysis shows that the recent highest accuracy models based on simple detection and the classification architectures are You Only Look Once (YOLO) and RetinaNet.
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References
Boyle, P., Levin, B., et al.: World cancer report 2008. IARC Press, International Agency for Research on Cancer (2008)
Al-antari, M.A., Al-masni, M.A., Park, S.U., Park, J.H., Metwally, M.K., Kadah, Y.M., Han, S.M., Kim, T.-S.: An automatic computer-aided diagnosis system for breast cancer in digital mammograms via deep belief network. J. Med. Biol. Eng. 38(3), 443–456 (2017)
Al-masni, M., Al-antari, M., Park, J., Gi, G., Kim, T., Rivera, P., Valarezo, E., Han, S.-M., Kim, T.-S.: Detection and classification of the breast abnormalities in digital mammograms via regional convolutional neural network. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017), Jeju Island, South Korea, pp. 1230–1236(2017)
Al-masni, M.A., Al-antari, M., Park, J.-M.P., Gi, G., Kim, T.-Y.K., Rivera, P., Valarezo, E., Choi, M.-T., Han, S.-M., Kim, T.-S.: Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput. Methods Programs Biomed. 157, 85–94 (2018)
Al-antari, M.A., Al-masni, M.A., Park, S.U., Park, J.H., Kadah, Y.M., Han, S.M., Kim, T.S.: Automatic computer-aided diagnosis of breast cancer in digital mammograms via deep belief network. In: Global Conference on Engineering and Applied Science (GCEAS), Japan, pp. 1306–1314 (2016)
Al-antari, M.A., Al-masni, M.A., Kadah, Y.M.: Hybrid model of computer-aided breast cancer diagnosis from digital mammograms. J. Sci. Eng. 04(2), 114–126 (2017)
Dromain, C., Boyer, B., Ferré, R., Canale, S., Delaloge, S., Balleyguier, C.: Computed aided diagnosis (CAD) in the detection of breast cancer. Eur. J. Radiol. 82(3), 417–423 (2013)
Dhungel, N., Carneiro, G., Bradley, A.P.: Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Australia (2015)
Wang, Y., Tao, D., Gao, X., Li, X., Wang, B.: Mammographic mass segmentation: embedding multiple features in vector-valued level set in ambiguous regions. Pattern Recognit. 44(9), 1903–1915 (2011)
Rahmati, P., Adler, A., Hamarneh, G.: Mammography segmentation with maximum likelihood active contours. Med. Image Anal. 16(9), 1167–1186 (2012)
Domínguez, A.R., Nandi, A.: Toward breast cancer diagnosis based on automated segmentation of masses in mammograms. Pattern Recognit. 42(6), 1138–1148 (2009)
Qiu, Y., Yan, S., Gundreddy, R.R., Wang, Y., Cheng, S., Liu, H., Zheng, B.: A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. J. X-Ray Sci. Technol. 25(5), 751–763 (2017)
Dhungel, N., Carneiro, G., Bradley, A.P.: A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med. Image Anal. 37(1), 114–128 (2017)
LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404 (1990)
Becker, A.S., Marcon, M., Ghafoor, S., Wurnig, M.C., Frauenfelder, T., Boss, A.: Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest. Radiol. 52(7), 434–440 (2017)
Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C.I., Mann, R., et al.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303–312 (2017)
Dhungel, N., Carneiro, G., Bradley, A.P.: Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2015)
Dhungel, N., Carneiro, G., Bradley, A.P.: A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med. Image Anal. 37, 114–128 (2017)
Akselrod-Ballin, A., Karlinsky, L., Alpert, S., Hasoul, S., Ben-Ari, R., Barkan, E.: A region based convolutional network for tumor detection and classification in breast mammography. In: Deep Learning and Data Labeling for Medical Applications, pp. 197–205. Springer (2016)
Akselrod-Ballin, A., Karlinsky, L., Hazan, A., Bakalo, R., Horesh, A.B., Shoshan, Y., et al.: Deep learning for automatic detection of abnormal findings in breast mammography. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 321–329. Springer (2017)
Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8(1), 4165 (2018)
Choukroun, Y., Bakalo, R., Ben-Ari, R., Akselrod-Ballin, A., Barkan, E., Kisilev, P.: Mammogram classification and abnormality detection from nonlocal labels using deep multiple instance neural network (2017)
Jalalian, A., Mashohor, S., Mahmud, H., Saripan, M., Rahman, A., Ramli, B., et al.: Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin. Imaging 37(3), 420–426 (2013)
Digital Database for Screening Mammography. http://www.eng.usf.edu/cvprg/Mammography/Database.html
Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: The digital database for screening mammography. In: Yaffe, M.J. (ed.) Proceedings of the Fifth International Workshop on Digital Mammography, pp. 212–218. Medical Physics Publishing (2001). ISBN 1–930524–00–5
Lee, R.S., Gimenez, F., Hoogi, A., Rubin, D.: Curated breast imaging subset of DDSM. Cancer Imaging Arch. 8 (2016)
Curated Breast Imaging Subset of DDSM. https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM
The Mini–MIAS Database of Mammograms. http://peipa.essex.ac.uk/info/mias.html
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. 19(2), 236–248 (2012)
Orel, S.G., Kay, N., Reynolds, C., Sullivan, D.C.: BI-RADS categorization as a predictor of malignancy. Radiology 211(3), 845–850 (1999)
The INbreast Dataset. http://medicalresearch.inescporto.pt/breastresearch/index.php/Get_INbreast_Database
The Breast Cancer Digital Repository. https://bcdr.eu/
Al-antari, M.A., Al-masni, M.A., Choi, M.-T., Han, S.-M., Kim, T.-S.: A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int. J. Med. Inform. 117, 44–54 (2018)
Jiang, F., Liu, H., Yu, S., Xie, Y.: Breast mass lesion classification in mammograms by transfer learning. In: Proceedings of the 5th International Conference on Bioinformatics and Computational Biology, pp. 59–62. ACM (2017)
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. 62(23), 8894 (2017)
Levy, D., Jain, A.: Breast mass classification from mammograms using deep convolutional neural networks. arXiv:1612.00542 (2016)
Yuan, Z.-W., Jun, Z.: Feature extraction and image retrieval based on AlexNet. In: Eighth International Conference on Digital Image Processing (ICDIP 2016), vol. 10033, p. 100330E. International Society for Optics and Photonics (2016)
Wu, C., Wen, W., Afzal, T., Zhang, Y., Chen, Y.: A compact DNN: approaching GoogleNet-level accuracy of classification and domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5668–5677 (2017)
Jung, H., Kim, B., Lee, I., Yoo, M., Lee, J., Ham, S., Woo, O., Kang, J.: Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PloS One 13(9), e0203355 (2018)
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Hamed, G., Marey, M.A.ER., Amin, S.ES., Tolba, M.F. (2020). Deep Learning in Breast Cancer Detection and Classification. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_30
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DOI: https://doi.org/10.1007/978-3-030-44289-7_30
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