Mamdani-Fuzzy Expert System for BIRADS Breast Cancer Determination Based on Mammogram Images

Baharuddin, Wan Noor Aziezan; Hussain, Rizuana Iqbal; Abdullah, Siti Norul Huda Sheikh; Fitri, Neno; Abdullah, Azizi

doi:10.1007/978-3-642-40567-9_9

Cited by 2 publications

(1 citation statement)

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“…To handle uncertainties machine learning like Type-2 Fuzzy Logic, and rough set theory are used to classify BIRADS [12,13,14]. These two methods are selected because of their ability to handle uncertainty and have been used to classify BIRADS based on extracted features from mammogram only [15].…”

Section: Introductionmentioning

confidence: 99%

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

Baharuddin

Abdullah

Sahran

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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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. To evaluate the proposed model, accuracy, specificity and sensitivity of the modal will be calculated and compared vis-à-vis rules given by the experts. The study encompasses a number of steps beginning with collection of the data from Radiology Department, Hospital of National University of Malaysia (UKM). 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 rules achieve 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.

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