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COMPUTER SCIENCE

Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images

ORCID Icon, , , , , , ORCID Icon & | (Reviewing editor) show all
Article: 1968324 | Received 10 Apr 2021, Accepted 26 Jul 2021, Published online: 09 Sep 2021

References

  • Abdul-Hadi, A. M., Abdulhussain, S. H., Mahmmod, B. M., & Pham, D. On the computational aspects of Charlier polynomials. (2020). Cogent Engineering Ed. by Duc Pham, 7(1), 1763553. https://doi.org/10.1080/23311916.2020.1763553
  • Abdulhussain, S., Al-Haddad, S. A. R., Saripan, M. I., Mahmmod, B. M., & Hussien, A. (2020). Fast Temporal video segmentation based on Krawtchouk-Tchebichef moments. IEEE Access, 8, 72347–19. https://doi.org/10.1109/ACCESS.2020.2987870
  • Abdulhussain, S. H., & Mahmmod, B. M. (2021). Fast and efficient recursive algorithm of Meixner polynomials. Journal of Real-Time Image Processing. https://doi.org/10.1007/s11554-021-01093-z
  • Abirami, C., Harikumar, R., & Chakravarthy, S. R. S. (2016). Performance analysis and detection of micro calcification in digital mammograms using wavelet features. In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 2327–2331), Chennai, India. IEEE.
  • Adel, M., Kotb, A., Farag, O., M. Saeed Darweesh, & Mostafa, H. (2019). Breast cancer diagnosis using image processing and machine learning for elastography images. In 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST) (pp. 1–4), Thessaloniki, Greece. IEEE.
  • Ahmad, L. G., Eshlaghy, A. T., Poorebrahimi, A., Ebrahimi, M., and Razavi, A. R. (2013). Using three machine learning techniques for predicting breast cancer recurrence. Journal of Medical Informatics, 4(124), 3. https://doi.org/10.4172/2157-7420.1000124
  • Bahya Hospital. Retrieved July 19, 2018, from https://www.baheya.org
  • Barr, R. G. (2012). Sonographic breast elastography: A primer. Journal of Ultrasound in Medicine, 31(5), 773–783. https://doi.org/10.7863/jum.2012.31.5.773
  • Becker, A. S., Marcon, M., Ghafoor, S., Wurnig, M. C., Frauenfelder, T., & Boss, A. (2017). Deep learning in mammography: Diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Investigative Radiology, 52(7), 434–440. https://doi.org/10.1097/RLI.0000000000000358
  • Bektas, B., Emre ,İ., Kartal, E., & Gulsecen, S. (2018). “Classification of mammography images by machine learning techniques”. In 2018 3rd International Conference on Computer Science and Engineering (UBMK) (pp. 580–585). IEEE.
  • Charan, S., Khan, M. J., & Khurshid, K. (2018). “Breast cancer detection in mammograms using convolutional neural network”. In: 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1–5), Sarajevo, Bosnia, and Herzegovina. IEEE.
  • Cheung, P., & Donlon, ca. 2016. MRI upgrades at radiology associates. San Luis Obispo: California Polytechnic State University.
  • EE, P., JE, J., & Db, P. 2005. SavingWomen’s lives: Strategies for improving breast cancer detection and diagnosis Institute of Medicine (US), National Research Council (US) Committee on New Approaches to Early Detection, and Diagnosis of Breast Cancer. National Academies Press (US.
  • Gbenga, D. E., Christopher, N., & Yetunde, D. C. (2017). Performance comparison of machine learning techniques for breast cancer detection. Nova, 6(1), 1–8. https://doi.org/10.20286/nova-jeas-060105
  • Gong, P., Marceau, D. J., & Howarth, P. J. (1992). A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data. Remote Sensing of Environment, 40(2), 137–151. https://doi.org/10.1016/0034-4257(92)90011-8
  • Han, Z., Haralick, R., Shanmugam, K.,& Dinstein (2017). Breast cancer multi-classification from histopathological images with structured deep learning model. Scientific Reports, 7(1), 1–10. https://doi.org/10.1109/TSMC.1973.4309314
  • Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6(6), 610–621. https://doi.org/10.1109/TSMC.1973.4309314
  • Joginipelly, A. et al. (2012). Efficient FPGA implementation of steerable Gaussian smoothers. In Proceedings of the 2012 44th Southeastern Symposium on System Theory (SSST) (pp. 78–82). IEEE,Jacksonville, FL, USA https://doi.org/10.1109/SSST.2012.6195131.
  • Joginipelly, A. K. 2014. Efficient FPGA architectures for separable filters and logarithmic multipliers and automation of fish feature extraction using Gabor filters. University of New Orleans Theses and Dissertations.
  • Kashif, M. (2020). Chapter 6 - Application of machine learning and image processing for detection of breast cancer. In M. D. Lytras, A. Sarirete, et al (Eds.), Innovation in health informatics ISBN: 978-0-12-819043-2. (pp. 145–162). Next Gen Tech Driven Personalized MedSmart Healthcare. Academic Press.
  • Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17. https://doi.org/10.1016/j.csbj.2014.11.005
  • Kowal, M., Filipczuk, P., Obuchowicz, A., Korbicz, J., & Monczak, R. (2013). Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Computers in Biology and Medicine, 43(10), 1563–1572. https://doi.org/10.1016/j.compbiomed.2013.08.003
  • Mahmmod, B. M., Abdul-Hadi, A. M., Abdulhussain, S. H., & Hussien, A. (2020). On computational aspects of Krawtchouk polynomials for high orders. Journal of Imaging, 6(8), 81. https://doi.org/10.3390/jimaging6080081
  • McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., Ledsam, J. R., & Shetty, S. (2020, January). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94. https://doi.org/10.1038/s41586-019-1799-6
  • MIAS database. Retrieved January 1, 2021. http://peipa.essex.ac.uk/info/mias.html
  • Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987. https://doi.org/10.1109/TPAMI.2002.1017623
  • Öztürk, Ş., & Akdemir, B. (2018). Application of feature extraction and classification methods for histopathological image using GLCM, LBP, LBGLCM, GLRLM and SFTA. Procedia Computer Science, 132, 40–46. https://doi.org/10.1016/j.procs.2018.05.057
  • Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., Ter Haar Romeny, B., Zimmerman, J. B., & Zuiderveld, K. (1987). Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, 39(3), 355–368. https://doi.org/10.1016/S0734-189X(87)80186-X.
  • Saber, A., Sakr, M., Abo-Seida, O. M., Keshk, A., & Chen, H. (2021). A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access, 9, 71194–71209. https://doi.org/10.1109/ACCESS.2021.3079204
  • Saraswathi, D., Dharani, D., & Srinivasan, E. (2016). “An efficient feature extraction technique for breast cancer diagnosis using curvelet transform and swarm intelligence”. In: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 441–445), Chennai, India. IEEE.
  • Setiawan, A. S., Wesley, J., Purnama, Y., & Purnama, Y. (2015). Mammogram classification using law’s texture energy measure and neural networks. Procedia Computer Science, 59, 92–97. https://doi.org/10.1016/j.procs.2015.07.341
  • Shi, P., Wu, C., Zhong ,J., & Wang, H. (2019). Deep learning from small dataset for BI-RADS density classification of mammography images. In 2019 10th International Conference on Information Technology in Medicine and Education (ITME), Qingdao, China (pp. 102–109). https://doi.org/10.1109/ITME.2019.00034.
  • Siegel, R. L., Miller, K., Sauer, A., Fedewa, S., Butterly, L., Anderson, J., Cercek, A., Smith, R., & Jemal, A. (2020). Colorectal cancer statistics, 2020. CA: a cancer journal for clinicians.
  • Siegel, R. L., Miller, K. D., & Jemal, A. (2016). Cancer statistics, 2016. CA: A Cancer Journal for Clinicians, 66(1), 7–30. https://doi.org/10.3322/caac.21332
  • Sreehari Sastry, S., Vindhya Kumari, Nageswara Rao, C., Mallika, K., Lakshminarayana, S., & Tiong, H. (2012). Transition temperatures of thermotropic liquid crystals from the local binary gray level cooccurrence matrix. Advances in Condensed Matter Physics. https://doi.org/10.1155/2012/527065
  • Unger-Saldaña, K. (2014). Challenges to the early diagnosis and treatment of breast cancer in developing countries. World Journal of Clinical Oncology, 5(3), 465. https://doi.org/10.5306/wjco.v5.i3.465
  • Warburg, O. (1956). On the origin of cancer cells. Science, 123(3191), 309–314. https://doi.org/10.1126/science.123.3191.309
  • Yu, X., Pang, W., Xu, Q., & Liang, M. (2020, September). Mammographic image classification with deep fusion learning. Scientific Reports, 10 (1), 14361. https://doi.org/10.1038/s41598-020-71431-x
  • Zhou, B.-G., Wang, D., Ren, W., Li, X., He, Y., Liu, B., Wang, Q., Chen, S., Alizad, A., & Xu, H. (2017). Value of shear wave arrival time contour display in shear wave elastography for breast masses diagnosis. Scientific Reports, 7(1), 1–9. https://doi.org/10.1038/s41598-017-07389-0