Type 2 Fuzzy Logic for mammogram breast tissue classification

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

doi:10.1109/iccsii.2016.7462439

Cited by 3 publications

(1 citation statement)

References 8 publications

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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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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.

Self Cite

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Moving towards green university: a method of analysis based on multi-criteria decision-making approach to assess sustainability indicators

Yadegaridehkordi

Nilashi

2022

Int. J. Environ. Sci. Technol.

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Classification of Breast Cancer in Mammograms Using an Optimized Hybrid Deep Learning Models and Feature Fusion Techniques

shanker¹

2023

Preprint

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Breast cancer is the most common deadly disease occurred in women. The major cause of the breast cancer agent is still not known. The early detection and treatment of breast cancer prevent the spreading of cancers to other parts and increase the lifetime of patients. Micro-calcification is one of the main signs of breast cancer. Mammography is a widely used digital screening approach to detect a microcalcification cluster in images. Compared to other image modalities, mammography is inexpensive and requires a low radiation dose. Image processing techniques with the aid of machine learning (ML) and deep learning (DL) techniques support radiologists to diagnose breast cancers earlier. In this work, the modified hybrid models with optimized feature selection models are proposed for accurate microcalcification classifications. The hybrid models of ResNet101V2 with Long short-term memory (LSTM) layers and ResNet101V2 with Support Vector Machine (SVM) classifiers are proposed for feature extraction. Then, the features are combined using serial-based feature fusion techniques. The performance of classifier models and feature selection process is improved using the metaheuristic algorithm of the Cheetahs optimizer. Experimental results on the MIAS breast cancer database show the superior performance of proposed models in terms of area under the curve (AUC), accuracy, and specificity and recall rates.

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Type 2 Fuzzy Logic for mammogram breast tissue classification

Cited by 3 publications

References 8 publications

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

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

Moving towards green university: a method of analysis based on multi-criteria decision-making approach to assess sustainability indicators

Classification of Breast Cancer in Mammograms Using an Optimized Hybrid Deep Learning Models and Feature Fusion Techniques

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