Breast Cancer Detection Using Hybrid Computational Intelligence Techniques

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Author(s): Debi Prasanna Acharjya (VIT University, India)and Chiranji Lal Chowdhary (VIT University, India)
Copyright: 2018
Pages: 30
Source title: Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics
Source Author(s)/Editor(s): Joseph Tan (McMaster University, Canada)
DOI: 10.4018/978-1-5225-5460-8.ch011

Keywords: Health Information Systems / Healthcare Administration / Medical Information Science Reference / Medicine & Healthcare

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Abstract

Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This chapter highlights two hybridizations pertaining to breast cancer. In one hybridization technique, it hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction methods. In the second case, intuitionistic fuzzy histogram hyperbolization is hybridized with possibilistic fuzzy c-mean clustering algorithm. Both hybridizations are studied to extract the region of interest and then to enhance the edges surrounding it. Experimental analysis is carried out for both models and an exhaustive study on these models is presented in this chapter.

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Breast Cancer Detection Using Hybrid Computational Intelligence Techniques

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