Implementation of Neuro-Fuzzy Decision Tree Based Malignant Tumor Detection System

Authors(1) :-Sanjeev Kumar

The paper designs a technique to classify the tumor as malignant or benign. The designed system works on various attributes of tumor like tumor thickness, shape, size etc. The classification process completes in three phases; the phase 1 classifies the attributes as cat1 or cat2 on the basis of information gain. Then in phase 2 cat1 attributes are used to select the class of tumor by using the RBF neural network while the cat2 attributes uses the fuzzy to select the class of tumor. The results of both techniques are collaborated by using the fuzzy inference system in the phase 3. The effectiveness of the technique is easily identified by using results. Finally Comparing accuracy between Neuro Fuzzy system and decision tree.

Authors and Affiliations

Sanjeev Kumar
ABESIT, Ghaziabad, Uttar Pradesh, India

Breast Cancer, Malignant, Benign, Tumor, RBF, Fuzzy, Decision Tree

  1. Ferlay, J., Shin, H. R., Bray, F., Forman, D., Mathers, C., & Parkin, D. M. (2010). Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008.International journal of cancer, 127(12), 2893-2917.
  2. Helvie, M. A., Chang, J. T., Hendrick, R. E., & Banerjee, M. (2014). Reduction in late?stage breast cancer incidence in the mammography era: Implications for overdiagnosis of invasive cancer. Cancer, 120(17), 2649-2656.
  3. Siegel, R., DeSantis, C., Virgo, K., Stein, K., Mariotto, A., Smith, T., ... & Ward, E. (2012). Cancer treatment and survivorship statistics, 2012. CA: a cancer journal for clinicians, 62(4), 220-241.
  4. Cheng, H. D., Cai, X., Chen, X., Hu, L., & Lou, X. (2003). Computer-aided detection and classification of microcalcifications in mammograms: a survey.Pattern recognition, 36(12), 2967-2991.
  5. Ma, Y., Tay, P. C., Adams, R. D., & Zhang, J. Z. (2010, September). A novel shape feature to classify microcalcifications. In Image Processing (ICIP), 2010 17th IEEE International Conference on (pp. 2265-2268). IEEE.
  6. Sickles, E. A. (1986). Breast calcifications: mammographic evaluation.Radiology, 160(2), 289-293.
  7. Arnold, R., Langer, P., Rothmund, M., Klöppel, G., Kann, P. H., Heverhagen, J. T., ... & Stinner, B. (2013). Endokrine Tumoren des gastroenteropankreatischen Systems. In Praxis der Viszeralchirurgie (pp. 497-628). Springer Berlin Heidelberg.
  8. Islam, M. J., Ahmadi, M., & Sid-Ahmed, M. A. (2010). An efficient automatic mass classification method in digitized mammograms using artificial neural network. arXiv preprint arXiv:1007.5129.
  9. Chan, H. P., Sahiner, B., Lam, K. L., Petrick, N., Helvie, M. A., Goodsitt, M. M., & Adler, D. D. (1998). Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. Medical Physics, 25(10), 2007-2019.
  10. I. Leichter, R. Lederman, S. Buchbinder, P. Bamberger, B.Novak and S. Fields, "Optimizing parameters for computeraided diagnosis of microcalcifications at mammography",
  11. Cheng, H. D., Cai, X., Chen, X., Hu, L., & Lou, X. (2003). Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern recognition, 36(12), 2967-2991.
  12. Moradmand, H., Setayeshi, S., & Targhi, H. K. (2012). Comparing methods for segmentation of microcalcification clusters in digitized mammograms. arXiv preprint arXiv:1201.5938.
  13. Ganesan, K., Acharya, U., Chua, C. K., Min, L. C., Abraham, K. T., & Ng, K. B. (2013). Computer-aided breast cancer detection using mammograms: a review. Biomedical Engineering, IEEE Reviews in, 6, 77-98.
  14. Paquerault, S., Yarusso, L. M., Papaioannou, J., Jiang, Y., & Nishikawa, R. M. (2004). Radial gradient-based segmentation of mammographic microcalcifications: observer evaluation and effect on CAD performance. Medical physics, 31(9), 2648-2657
  15. Heinlein, P., Drexl, J., & Schneider, W. (2003). Integrated wavelets for enhancement of microcalcifications in digital mammography. Medical Imaging, IEEE Transactions on, 22(3), 402-413.
  16. Unser, M., Aldroubi, A., & Laine, A. (2003). Guest editorial: wavelets in medical imaging. IEEE Transactions on Medical Imaging, 22(LIB-ARTICLE-2003-004), 285-288.
  17. Shinde, M. (2003). Computer aided diagnosis in digital mammography: Classification of mass and normal tissue (Doctoral dissertation, University of South Florida).
  18. Renato Campanini, Danilo Dongiovanni, Allessandro Riccardi, “A Novel Featureless Approach to Mass Detections in Digital Mammograms based on Support Vector Machines”, Department of Physics, University of Bologna, Italy, 2003.

Publication Details

Published in : Volume 2 | Issue 7 | September 2017
Date of Publication : 2017-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 08-15
Manuscript Number : CSEIT174402
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sanjeev Kumar, "Implementation of Neuro-Fuzzy Decision Tree Based Malignant Tumor Detection System", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.08-15, September-2017.
Journal URL : http://ijsrcseit.com/CSEIT174402

Article Preview

Follow Us

Contact Us