Breast tissue classification via interval type 2 fuzzy logic based rough set

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)1792-1802
Number of pages11
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume8
Issue number4-2
Publication statusPublished - 1 Jan 2018

Fingerprint

Fuzzy Logic
fuzzy logic
Fuzzy logic
breasts
Breast
Hospital Radiology Department
Tissue
Malaysia
Statistical Models
Information Systems
Noise
Sensitivity and Specificity
Rough set theory
Membership functions
Radiology
radiology
tissues
Therapeutics
Statistical methods
Classifiers

Keywords

  • Breast cancer
  • Classification
  • Fuzzy logic
  • Mammogram
  • Rough set

ASJC Scopus subject areas

  • Computer Science(all)
  • Agricultural and Biological Sciences(all)
  • Engineering(all)

Cite this

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title = "Breast tissue classification via interval type 2 fuzzy logic based rough set",
abstract = "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-{\`a}-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.",
keywords = "Breast cancer, Classification, Fuzzy logic, Mammogram, Rough set",
author = "Baharuddin, {Wan Noor Aziezan} and {Sheikh Abdullah}, {Siti Norul Huda} and Shahnorbanun Sahran and Ashwaq Qasem and {Iqbal Hussain}, Rizuana and Azizi Abdullah",
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AU - Baharuddin, Wan Noor Aziezan

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AU - Sahran, Shahnorbanun

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AU - Iqbal Hussain, Rizuana

AU - Abdullah, Azizi

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