Breast Tissue Classification via Interval Type 2 Fuzzy Logic Based Rough Set

Wan Noor Aziezan Baharuddin, Siti Norul Huda Sheikh Abdullah, Shahnorbanun Sahran, Ashwaq Qasem, Rizuana Iqbal Hussain, Azizi Abdullah


BIRADS is a Breast Imaging, Reporting and Data System. A tool to standardize mammogram reports and minimizes ambiguity during mammogram image evaluation. Classification of BIRADS is one of the most challenging tasks to radiologist. An apt treatment can be administered to the patient by the oncologist upon acquiring sufficient information at BIRADS stage. This study aspired to build a model, which classifies BIRADS using mammograms images and reports. Through the implementation of type-2 fuzzy logic as classifier, an automatically generated rules will be applied to the model. Comparison of accuracy, specificity and sensitivity of the modal will be performed vis-à-vis rules given by the experts. The study encompasses a number of steps beginning with collection of the data from Radiology Department of National University of Malaysia Medical Center. The data was initially processed to remove noise and gaps. Then, an algorithm developed by selecting type-2 fuzzy logic using Mamdani model. Three types of membership functions were employed in the study. Among the rules that used by the model were obtained from experts as well as generated automatically by the system using rough set theory. Finally, the model was tested and trained to get the best result. The study shows that triangular membership function based on rough set rules obtains 89% whereas expert driven rules gains about 78% of accuracy rates. The sensitivity using expert rules is 98.24% whereas rough set rules obtained 93.94%. Specificity for using expert rules and rough set rules are 73.33%, 84.34% consecutively. Conclusion: Based on statistical analysis, the model which employed rules generated automatically by rough set theory fared better in comparison to the model using rules given by the experts. 


Breast cancer; classification; fuzzy logic; mammogram; rough set

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National Cancer Registry Report, N, 2007. Malaysia Cancer Statistic – Data and Figure.

Bozek, J., Delac, K. &Grgic, M, 2008. Computer-Aided Detection and Diagnosis of Breast Abnormalities in Digital Mammography (September), 10–12.

Akay, M. F, 2009. Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications, 36(2), 3240–3247. doi:10.1016/j.eswa.2008.01.009

Sameti, M, 1998. Detection of Soft Tissue Abnormalities in Mammographic Images for Early Diagnosis of Breast Cancer the University of British Columbia. Retrieved from

Silverstein, M. J., Lagios, M. D., Recht, A., Allred, D. C., Harms, S. E., Holland, R., Holmes, D. R. et al, 2005. Image-detected breast cancer: state of the art diagnosis and treatment. Journal of the American College of Surgeons, 201(4), 586–97.

Kerlikowske, K., Carney, P. A., Geller, B., Mandelson, M. T., Taplin, S. H., Malvin, K., Ernster, V. et al, 2000. Performance of screening mammography among women with and without a first-degree relative with breast cancer. Annals of internal medicine, 133(11), 855–63. Retrieved from

R. M. Rangayyan, F. J. Ayres, J. L. Desautels, "A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs", Journal of the Franklin Institute, vol. 344, pp. 312-348, 2007.

Fenton, J. J., Taplin, S. H., Carney, P. A., Abraham, L., Sickles, E. A., D'orsi, C., & Elmore, J. G. (2007). Influence of computer-aided detection on performance of screening mammography. New England Journal of Medicine, 356(14), 1399-1409.

Qasem, A., Abdullah, S. N. H. S., Sahran, S., Wook, T. S. M. T., Hussain, R. I., Abdullah, N., & Ismail, F. (2014, March). Breast cancer mass localization based on machine learning. In Signal Processing & its Applications (CSPA), 2014 IEEE 10th International Colloquium on (pp. 31-36). IEEE.

Kamaruddin, N. (2017). Active Contour Model Using Fractional Sync Wave Function for Medical Image Segmentation. Asia-Pacific Journal of Information Technology and Multimedia, Vol. 5(2).

Dubey, A.K., Gupta, U. and Jain, S., 2018. Comparative Study of K-means and Fuzzy C-means Algorithms on The Breast Cancer Data. International Journal on Advanced Science, Engineering and Information Technology, 8(1), 18-29.

Baharuddin, W. N. A., Hussain, R. I., Abdullah, S. N. H. S., Fitri, N., & Abdullah, A. (2013). Mamdani-fuzzy expert system for BIRADS breast cancer determination based on mammogram images. In Soft Computing Applications and Intelligent Systems (pp. 99-110). Springer, Berlin, Heidelberg.

Baharuddin, W. N. A., Abdullah, S. N. H. S., Sahran, S., Qasem, A., bin Abdullah, A., Iqbal, R., & Ismail, F. (2016, March). Type 2 Fuzzy Logic for mammogram breast tissue classification. In Industrial Informatics and Computer Systems (CIICS), 2016 International Conference on (pp. 1-6). IEEE.

Hamouda, S.K.M., Wahed, M.E., Alez, R.H.A. and 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.

Hassanien, AboulElla. 2007. Fuzzy rough sets hybrid scheme for breast cancer detection. Image and vision computing, Vol. 25.2, pp. 172-183.

Tan, P., Steinbach, M. &Vipin, K. 2006. Introduction to Data Mining Instructor’ s Solution Manual.

Han, J. &Kamber, M. 2006. Data Mining Concepts and Techniques pp.1–723

Caramihai, M., Severin, I., Blidaru, A., Balan, H., Davila, C. & Saptefrati, C. 2011. Evaluation of breast cancer risk by using fuzzy logic. Proceedings of the 10th WSEAS international conference on applied informatics and communications, Vol. 73, pp. 37–42.



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