Abstract
Breast cancer is one of the most dangerous cancers and with the tremendous increase in the mammograms taken daily, computer-aided diagnosis systems play an important role for a fast and accurate prediction. In this paper, we propose three phases to detect and classify breast tumors. First, is the data preparation for converting DICOM files to images without losing data. Then, they are divided into mammograms with large and small masses representing the input to the second model training phase. The third phase is the model evaluation through two testing levels, first is the large masses checking and the second level is the small masses checking to output the detection results for large and small masses. The two testing levels using the trained small and large masses model overcomes the recent YOLO based detection work and the combined sizes trained model by achieving an overall accuracy of 89.5%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Boyle, P., Levin, B., et al.: World Cancer Report 2008. IARC Press, International Agency for Research on Cancer, Lyon (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.A., Park, J.M., 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., Gi, G., Kim, T.Y.K., Rivera, P., Valarezo, E., Choi, M.T., Han, S.M., Kim, T.S.: Simultaneous detection and classifi-cation of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput. Meth. Prog. 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, 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)
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)
Domnguez, 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)
Hamed, G., Marey, M.A.E.R., Amin, S.E.S., Tolba, M.F.: Deep learning in breast cancer detection and classification. In: Joint European-US Workshop on Applications of Invariance in Computer Vision, pp. 322–333. Springer, Cham (2020)
Hamed, G., Marey, M., Amin, S.E.S. and Tolba, M.F.: A Proposed Model for denoising breast mammogram images. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), pp. 652–657. IEEE December 2018
Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4), 198–211 (2007)
Van Ginneken, B., ter Haar Romeny, B.M., Viergever, M.: Computer-aided diagnosis in chest radiography: a survey. IEEE Trans. Med. Imaging 20(12), 1228–1241 (2001)
Jiang, Y., Nishikawa, R.M., Schmidt, R.A., et al.: Improving breast cancer diagnosis with computer-aided diagnosis. Acad. Radiol. 6(1), 2233 (1999)
Chan, H.-P., Doi, K., Vybrony, C.J., et al.: Improvement in radiologists detection of clustered microcalcifications on mammograms: the potential of computer aided diagnosis. Invest. Radiol. 25(10), 1102–1110 (1990)
Dhungel, N., Carneiro, G., Bradley, A.P.: 2015. Automated mass detection from mammograms using deep learning and random forest. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA) (2018). https://doi.org/10.1109/dicta.2015.7371234
Jiao, Z., Gao, X., Wang, Y., Li, J.: A deep feature based framework for breast masses classification. Neurocomputing 197, 221–231 (2016)
Suzuki, S., Zhang, X., Homma, N., Ichiji, K., Sugita, N., Kawasumi, Y., Ishibashi, T., Yoshizawa, M.: Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis. In: Proceedings of the SICE Annual Conference 2016, Tsukuba, Japan, pp. 1382–1386 (2016)
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, pp. 197–205. Springer, Cham (2016)
Wu, N., Phang, J., Park, J., Shen, Y., Huang, Z., Zorin, M., Jastrzbski, S., et al.: Deep neural networks improve radiologists performance in breast cancer screening. IEEE Trans. Med. Imaging 39(4), 1184–1194 (2019)
Jiang F.: Breast mass lesion classification in mammograms by transfer learn-ing. In: ICBCB17, Hong Kong, pp 59–62 (2017). https://doi.org/10.1145/3035012.3035022
Rodriguez-Ruiz, A., Lng, K., Gubern-Merida, A., Broed-ers, M., Gennaro, G., Clauser, P., Thomas, H.H., et al.: Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radi-ologists. JNCI: J. Natl. Cancer Inst. 111(9), 916–922 (2019)
Moreira, I., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M., Cardoso, J.: INbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)
Lee, R.S., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.L.: A curated mammography dataset for use in computer-aided detection and diagnosis research. Sci. Data 4, 170–177 (2017)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hamed, G., Marey, M.A.ER., Amin, S.ES., Tolba, M.F. (2021). The Mass Size Effect on the Breast Cancer Detection Using 2-Levels of Evaluation. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-58669-0_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58668-3
Online ISBN: 978-3-030-58669-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)