89
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Fusing clinical and image data for detecting the severity of breast cancer by a novel hierarchical approach

ORCID Icon, ORCID Icon & ORCID Icon
Pages 207-230 | Received 19 Jul 2019, Accepted 19 Jul 2021, Published online: 13 Feb 2022

References

  • Addeh, J., & Ebrahimzadeh, A. (2012). Breast cancer recognition using a novel hybrid intelligent method. Journal of Medical Signals and Sensors, 2(2), 95. https://doi.org/10.4103/2228-7477.110446
  • Ahmad, A., & Dey, L. (2007). A k-mean clustering algorithm for mixed numeric and categorical data. Data & Knowledge Engineering, 63(2), 503–527. https://doi.org/10.1016/j.datak.2007.03.016
  • Ahmadi, A., & Afshar, P. (2016). Intelligent breast cancer recognition using particle swarm optimization and support vector machines. Journal of Experimental & Theoretical Artificial Intelligence, 28(6), 1021–1034. https://doi.org/10.1080/0952813X.2015.1055828
  • Akgul, Y. S., & Kambhamettu, C. (2003). A coarse-to-fine deformable contour optimization framework. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2), 174–186. https://doi.org/10.1109/TPAMI.2003.1177150
  • Al-Khalidi, M. A. M., Bakr, M. A. H. A., Al-Attar, H. M., & Mahra, N. K. (2021). Breast Cancer Prediction. Breast Cancer, 5(3), 52–60. http://dstore.alazhar.edu.ps/xmlui/bitstream/handle/123456789/2693/IJAHMR210311.pdf?sequence=1&isAllowed=y
  • Alwan, N. A. (2016). Breast cancer among Iraqi women: Preliminary findings from a regional comparative Breast Cancer Research Project. Journal of Global Oncology, 2(5), 255. https://doi.org/10.1200/JGO.2015.003087
  • An, Y. Y., Kim, S. H., Kang, B. J., & Lee, A. W. (2015). Comparisons of Positron Emission Tomography/Computed Tomography and Ultrasound Imaging for Detection of Internal Mammary Lymph Node Metastases in Patients With Breast Cancer and Pathologic Correlation by Ultrasound-Guided Biopsy Procedures. Journal of Ultrasound in Medicine, 34(8), 1385–1394. https://doi.org/10.7863/ultra.34.8.1385
  • Arasu, V. A., Joe, B. N., Lvoff, N. M., Leung, J. W., Brenner, R. J., Flowers, C. I., Moore, D. H., & Sickles, E. A. (2012). Benefit of semiannual ipsilateral mammographic surveillance following breast conservation therapy. Radiology, 264(2), 371–377. https://doi.org/10.1148/radiol.12111458
  • Bagaria, S. P., Ray, P. S., Sim, M.-S., Ye, X., Shamonki, J. M., Cui, X., & Giuliano, A. E. (2014). Personalizing Breast Cancer Staging by the Inclusion of ER, PR, and HER2. JAMA Surgery, 149(2), 125–129. https://doi.org/10.1001/jamasurg.2013.3181
  • Balanică, V., Dumitrache, I., Caramihai, M., Rae, W., & Herbst, C. (2011). Evaluation of breast cancer risk by using fuzzy logic. University Politehnica of Bucharest Scientific Bulletin. Series C, 73(1), 53–64.
  • Basha, S. S., & Prasad, K. S. (2009). Automatic detection of breast cancer mass in mammograms using morphological operators and Fuzzy C-means clustering. Journal of Theoretical & Applied Information Technology, 5, 6.
  • Bazila Banu, A., & Thirumalaikolundusubramanian, P. (2018). Comparison of Bayes classifiers for breast cancer classification. Asian Pacific Journal of Cancer Prevention: APJCP, 19(10), 2917. https://doi.org/10.22034/APJCP.2018.19.10.2917
  • Bengio, Y., & LeCun, Y. (2007). Scaling learning algorithms towards A.I. Large-scale Kernel Machines, 34(5), 1–41. https://pdfs.semanticscholar.org/f01e/080777b59d6978e412ded8995edabbaa62f0.pdf
  • Borvayeh, A. J., Malakooti, B. M. V., & Hashemitaba, C. N. Study on Breast Cancer Detection Using Computer Systems.
  • Cancer Imaging Archive. (n.d.). http://www.cancerimagingarchive.net/
  • Chaurasia, V., & Pal, S. (2017) Data mining techniques: To predict and resolve breast cancer survivability.
  • Chaurasia, V., Pal, S., & Tiwari, B. B. (2018). Prediction of benign and malignant breast cancer using data mining techniques. Journal of Algorithms & Computational Technology, 12(2), 119–126. https://doi.org/10.1177/1748301818756225
  • Cherif, W. (2018). Optimization of K-NN algorithm by clustering and reliability coefficients: Application to breast-cancer diagnosis. Procedia Computer Science, 127, 293–299. https://doi.org/10.1016/j.procs.2018.01.125
  • Chuang, C. H., Huang, C. S., Ko, L. W., & Lin, C. T. (2015). An EEG-based perceptual function integration network for application to drowsy driving. Knowledge-Based Systems, 80, 143–152. https://doi.org/10.1016/j.knosys.2015.01.007
  • Chuquicusma, M. J., Hussein, S., Burt, J., & Bagci, U. (2018) April. How to fool radiologists with generative adversarial networks? A visual Turing test for lung cancer diagnosis. In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (pp. 240–244). IEEE, Washington, DC, USA.
  • Dhungel, N., Carneiro, G., & Bradley, A. P. (2015) October. Deep learning and structured prediction for the segmentation of mass in mammograms. In International Conference on Medical image computing and computer-assisted intervention (pp. 605–612). Munich, Germany, Springer, Cham.
  • Einipour, A. (2011). A fuzzy-ACO method for detect breast cancer. Global Journal of Health Science, 3(2), 195. https://doi.org/10.5539/gjhs.v3n2p195
  • Gallego-Posada, J., Montoya-Zapata, D. A., Quintero-Montoya, O. L., & Montoya-Zapa, D. A. (2016) Detection and diagnosis of breast tumors using deep convolutional neural networks. In Conference Proceedings of XVII Latin American Conference in Automatic Control (p. 17),  Medellín, Colombia.
  • Ganesan, K., Acharya, U. R., Chua, C. K., Min, L. C., Abraham, K. T., & Ng, K.-H. (2013). Computer-aided breast cancer detection using mammograms: A review. IEEE Reviews in Biomedical Engineering, 6, 77–98. https://doi.org/10.1109/RBME.2012.2232289
  • Gutierrez-Rodríguez, A. E., Martínez-Trinidad, J. F., García-Borroto, M., & Carrasco-Ochoa, J. A. (2015). Mining patterns for clustering on numerical datasets using unsupervised decision trees. Knowledge-Based Systems, 82, 70–79. https://doi.org/10.1016/j.knosys.2015.02.019
  • Hamashima, C., Ohta, K., Kasahara, Y., Katayama, T., Nakayama, T., Honjo, S., & Ohnuki, K. (2015). A meta-analysis of mammographic screening with and without clinical breast examination. Cancer Science, 106(7), 812–818. https://doi.org/10.1111/cas.12693
  • Hamouda, S. K. M., Wahed, M. E., Alez, R. H. A., & Riad, K. (2018). Robust breast cancer prediction system based on rough set theory at National Cancer Institute of Egypt. Computer Methods and Programs in Biomedicine, 153, 259–268. https://doi.org/10.1016/j.cmpb.2017.10.016
  • Han, J., Kamber, M., & Mining, D. (2001). Concepts and techniques. Morgan Kaufmann, 340, 94104–3205.
  • Irvine Machine, U. C., & Repository, L. (n.d.). University of California Irvine. http://archive.ics.uci.edu/ml/index.php
  • Islam, M. M., Haque, M. R., Iqbal, H., Hasan, M. M., Hasan, M., & Kabir, M. N. (2020). Breast cancer prediction: A comparative study using machine learning techniques. S.N. Computer Science, 1(5), 1–14. https://doi.org/10.1007/s42979-020-00305-w
  • Javanmard, R., JeddiSaravi, K., & Alinejad-Rokny, H. (2013). Proposed a new method for rules extraction using artificial neural network and artificial immune system in cancer diagnosis. Journal of Bionanoscience, 7(6), 665–672. https://doi.org/10.1166/jbns.2013.1160
  • Jen, -C.-C., & Yu, -S.-S. (2015). Automatic detection of abnormal mammograms in mammographic images. Expert Systems with Applications, 42(6), 3048–3055. https://doi.org/10.1016/j.eswa.2014.11.061
  • Juang, L.-H., & Wu, M.-N. (2010). MRI brain lesion image detection based on color-converted K-means clustering segmentation. Measurement, 43(7), 941–949. https://doi.org/10.1016/j.measurement.2010.03.013
  • Kumar, U. K., Nikhil, M. S., & Sumangali, K. (2017) August. Prediction of breast cancer using voting classifier technique. In 2017 IEEE international conference on smart technologies and management for computing, communication, controls, energy and materials (ICSTM) (pp. 108–114). IEEE, Tamil Nadu, India.
  • Larose, D. T. (2004). Decision trees. In Daniel T. Larose (Ed.), Discovering knowledge in data: An introduction to data mining (pp. 108–126). Wiley Interscience. https://www.amazon.com/Discovering-Knowledge-Data-Introduction-Mining/dp/0471666572
  • Lavanya, D. & Rani, K. U. (2012). Ensemble decision tree classifier for breast cancer data. International Journal of Information Technology Convergence and Services, 2(1), 17. https://doi.org/10.5121/ijitcs.2012.2103
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015, May). Deep learning. Nature, 521 (7553), 436. 2015 https://doi.org/10.1038/nature14539
  • Lee, S. C., Jain, P. A., Jethwa, S. C., Tripathy, D., & Yamashita, M. W. (2014). Radiologist’s role in breast cancer staging: Providing key information for clinicians. Radiographics, 34(2), 330–342. https://doi.org/10.1148/rg.342135071
  • Li, Y., Hu, Z., Cai, Y., & Zhang, W. (2005) August. Support vector based prototype selection method for nearest neighbor rules. In International Conference on Natural Computation (pp. 528–535), Changsha, China. Springer, Berlin, Heidelberg.
  • Lin, W.-C., Ke, S.-W., & Tsai, C.-F. (2015). CANN: An intrusion detection system based on combining cluster centers and nearest neighbors. Knowledge-based Systems, 78, 13–21. https://doi.org/10.1016/j.knosys.2015.01.009
  • Merritt, C. R. B. (2009). Combined Screening With Ultrasound and Mammography vs. Mammography Alone in Women at Elevated Risk of Breast Cancer: Berg WA, for the ACRIN 6666 Investigators (Johns Hopkins Green Spring, Lutherville, MD; et al.) JAMA 299: 2151-2163, 2008. Breast Diseases: A Y.B. Quarterly, 20(4), 401–402.
  • Miller, A. B., Wall, C., Baines, C. J., Sun, P., To, T., & Narod, S. A. (2014). Twenty five year follow-up for breast cancer incidence and mortality of the Canadian National Breast Screening Study: Randomised screening trial. BMJ, 348(feb11 9), g366. https://doi.org/10.1136/bmj.g366
  • Minaei-Bidgoli, B., Asadi, M., & Parvin, H. (2011). An ensemble based approach for feature selection. In engineering applications of neural networks Springer.
  • Minaei-Bidgoli, B., Parvin, H., Alinejad-Rokny, H., Alizadeh, H., & Punch, W. F. (2014). Effects of resampling method and adaptation on clustering ensemble efficacy. Artificial Intelligence Review, 41(1), 27–48. https://doi.org/10.1007/s10462-011-9295-x
  • Mohammadi, G., Rezaee, K., Hajimani, M., Masoomi, N., & Karimi, M. (2012), A Novel Intelligent system for Exact Positioning of Breast Cancer Tumors using Contourlet and Image Processing, Conference IKT2012 At: Babol, Iran
  • Parvin, H., Minaei-Bidgoli, B., & Alinejad-Rokny, H. (2013). A new imbalanced learning and dictions tree method for breast cancer diagnosis. Journal of Bionanoscience, 7(6), 673–678. https://doi.org/10.1166/jbns.2013.1162
  • Parvin, H., Minaei-Bidgoli, B., Alinejad-Rokny, H., & Punch, W. F. (2013). Data weighing mechanisms for clustering ensembles. Computers & Electrical Engineering, 39(5), 1433–1450. https://doi.org/10.1016/j.compeleceng.2013.02.004
  • Parvin, H., MirnabiBaboli, M., & Alinejad-Rokny, H. (2015). Proposing a classifier ensemble framework based on classifier selection and decision tree. Engineering Applications of Artificial Intelligence, 37, 34–42. https://doi.org/10.1016/j.engappai.2014.08.005
  • Sakri, S. B., Rashid, N. B. A., & Zain, Z. M. (2018). Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access, 6, 29637–29647. https://doi.org/10.1109/ACCESS.2018.2843443
  • Sangeetha, N. M., & Pugazhenthi, D. (2014). Evaluation of breast percent density in digital mammography images using fuzzy C-means clustering and support vector machine. Evaluation, 2(3).
  • Sarvestani, A. S., Safavi, A. A., Parandeh, N. M., & Salehi, M. (2010) October. Predicting breast cancer survivability using data mining techniques. In 2010 2nd International Conference on Software Technology and Engineering (Vol. 2, pp. V2–227). IEEE, San Juan, PR, USA.
  • Shasidhar, M., Raja, V. S., & Kumar, B. V. (2011) June MRI brain image segmentation using modified fuzzy C-means clustering algorithm. In 2011 International Conference on Communication Systems and Network Technologies (pp. 473–478). IEEE, Katra, India.
  • Taher, F., & Sammouda, R. (2011) February. Lung cancer detection by using artificial neural network and fuzzy clustering methods. In 2011 IEEE GCC Conference and Exhibition (GCC) (pp. 295–298). IEEE, Dubai, United Arab Emirates.
  • Tapak, L., Shirmohammadi-Khorram, N., Amini, P., Alafchi, B., Hamidi, O., & Poorolajal, J. (2018). Prediction of survival and metastasis in breast cancer patients using machine learning classifiers. Clinical Epidemiology and Global Health.
  • Valente, S. A., Levine, G. M., Silverstein, M. J., Rayhanabad, J. A., Weng-Grumley, J. G., Ji, L., Holmes, D. R., Sposto, R., & Sener, S. F. (2012). Accuracy of predicting axillary lymph node positivity by physical examination, mammography, ultrasonography, and magnetic resonance imaging. Annals of Surgical Oncology, 19(6), 1825–1830. https://doi.org/10.1245/s10434-011-2200-7
  • Wang, H., Zheng, B., Yoon, S. W., & Ko, H. S. (2018). A support vector machine-based ensemble algorithm for breast cancer diagnosis. European Journal of Operational Research, 267(2), 687–699. https://doi.org/10.1016/j.ejor.2017.12.001
  • Wu, J., & Hicks, C. (2021). Breast Cancer Type Classification Using Machine Learning. Journal of Personalized Medicine, 11(2), 61. https://doi.org/10.3390/jpm11020061
  • Wu, M. N., Lin, C. C., & Chang, C. C. (2007) November. Brain tumor detection using color-based k-means clustering segmentation. In Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) (Vol.2, pp. 245–250). IEEE, Kaohsiung, Taiwan.
  • Xin, L., Chen, L., Zhang, H., Liu, Q., Xu, L., Wang, B., Li, T., Duan, X., & Liu, Y. (2014). Analysis of detecting value of ultrasound and the clinic-pathological features of axillary metastasis in breast cancer. Zhonghua Wai Ke Za Zhi [Chinese Journal of Surgery], 52(12), 924–928.
  • Yi, M., Mittendorf, E. A., Cormier, J. N., Buchholz, T. A., Bilimoria, K., Sahin, A. A., Hortobagyi, G. N., Gonzalez-Angulo, A. M., Luo, S., Buzdar, A. U., Crow, J. R., Kuerer, H. M., & Hunt, K. K. (2011). Novel staging system for predicting disease-specific survival in patients with breast cancer treated with surgery as the first intervention: Time to modify the current American Joint Committee on Cancer staging system. Journal of Clinical Oncology, 29(35), 4654. https://doi.org/10.1200/JCO.2011.38.3174
  • Zhang, Y.-N., Wang, C.-J., Xu, Y., Zhu, Q.-L., Zhou, Y.-D., Zhang, J., Mao, F., Jiang, Y.-X., & Sun, Q. (2015). Sensitivity, specificity and accuracy of ultrasound in diagnosis of breast cancer metastasis to the axillary lymph nodes in Chinese Patients. Ultrasound in Medicine & Biology, 41(7), 1835–1841. https://doi.org/10.1016/j.ultrasmedbio.2015.03.024

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.