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Fuzzy and Intuitionistic Fuzzy Operators with Applications in Breast Tumor Classification
Abstract
Breast Cancer accounting for 14% of all cancers in women is the most familiar cancer among women in India. According to GLOBOCON data 2018, 1,62,468 new cases and 87,090 demises were accounted for breast cancer in India. The incidence rate of breast cancer starts rising in the early 30s and reaches a peak at the age of 50–64 years. In general, 1 in 28 women will develop breast cancer at some point in their lives. For the early detection of breast cancer fine needle aspirate (FNA), technology and mammography are widely used. In this study, fuzzy and intuitionistic fuzzy aggregation operators are discussed along with their applications in breast cancer classification. Basically, aggregation operators use Laplace or normal distribution to collect vague information about breast tumors. Along with tumor label i.e., malignant, or benign, this aggregated information is utilized to train logistic regression, support vector machine (SVM) and nearest neighbor classifiers. These aggregation operators are efficient in improving classification accuracy and can be applied to all types of breast cancer images dataset for the early finding of breast cancer.
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