A Hybrid Scheme for Breast Cancer Detection Using Intuitionistic Fuzzy Rough Set Technique

Biometrics ◽  
2017 ◽  
pp. 1195-1219 ◽  
Author(s):  
Chiranji Lal Chowdhary ◽  
D. P. Acharjya
Keyword(s):  
Feature Extraction ◽  
Rough Set ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Hybrid Scheme ◽  
Fuzzy Rough Set ◽  
Intuitionistic Fuzzy ◽  
Prime Concern ◽  
Diagnosis Of Cancer ◽  
Occurrence Matrix

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 paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques. The hybrid scheme starts with image segmentation using intuitionistic fuzzy set to extract the zone of interest and then to enhance the edges surrounding it. Further feature extraction using gray-level co-occurrence matrix is presented. Additionally, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. The experimental analysis shows the overall accuracy of 98.3% and it is higher than the accuracy achieved by hybridizing fuzzy rough set model.

A Hybrid Scheme for Breast Cancer Detection using Intuitionistic Fuzzy Rough Set Technique

2016 ◽  
Vol 11 (2) ◽  
pp. 38-61 ◽  
Author(s):  
Chiranji Lal Chowdhary ◽  
D. P. Acharjya
Keyword(s):  
Feature Extraction ◽  
Rough Set ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Hybrid Scheme ◽  
Fuzzy Rough Set ◽  
Intuitionistic Fuzzy ◽  
Prime Concern ◽  
Diagnosis Of Cancer ◽  
Occurrence Matrix

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 paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques. The hybrid scheme starts with image segmentation using intuitionistic fuzzy set to extract the zone of interest and then to enhance the edges surrounding it. Further feature extraction using gray-level co-occurrence matrix is presented. Additionally, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. The experimental analysis shows the overall accuracy of 98.3% and it is higher than the accuracy achieved by hybridizing fuzzy rough set model.

MODELING OF COGNITIVE STRUCTURE OF UNCERTAIN SCIENTIFIC CONCEPTS USING FUZZY-ROUGH SETS AND INTUITIONISTIC FUZZY SETS: EXAMPLE OF THE LIFE CONCEPT

2009 ◽  
Vol 17 (05) ◽  
pp. 747-769 ◽  
Author(s):  
NURETTIN YOREK ◽  
SERKAN NARLI
Keyword(s):  
Rough Set ◽  
Fuzzy Set ◽  
Structural Model ◽  
Intuitionistic Fuzzy Set ◽  
Cognitive Structure ◽  
Human Motion ◽  
Scientific Uncertainty ◽  
Structured Interviews ◽  
Fuzzy Rough Set ◽  
Intuitionistic Fuzzy

In this study, using fuzzy-rough set and intuitionistic fuzzy set approaches, we propose a cognitive structural model for the concept of life for which a certain definition can not be made because of scientific uncertainty as well as moral, legal, and theological aspects. Total 191 first-year students from seven different high schools in a large western city in Turkey participated in the study. An open-ended conceptual understanding (CULC) test, developed by the researcher, was used for data collection. Semi-structured interviews were carried out with 14 students and their biology teachers to clarify ambiguous points in students' responses to the CULC test. The results of analyses indicated that students constructed the concept of life by associating it predominantly with 'human'. Motion appeared as the most frequently associated term with the concept of life. The results suggest that the life concept has been constructed using animistic-anthropocentric cognitive schemes. In the next step, we evaluated the data obtained from the CULC test using the fuzzy-rough set and intuitionistic fuzzy set theories. Consequently, we propose an 'animistic-anthropocentric structural model' about cognitive construction of the concept of life.

Breast Cancer Detection Using Hybrid Computational Intelligence Techniques

2018 ◽  
pp. 251-280
Author(s):  
Debi Prasanna Acharjya ◽  
Chiranji Lal Chowdhary
Keyword(s):  
Breast Cancer ◽  
Clustering Algorithm ◽  
Intuitionistic Fuzzy Set ◽  
Region Of Interest ◽  
Extraction Methods ◽  
Intuitionistic Fuzzy ◽  
Prime Concern ◽  
Diagnosis Of Cancer ◽  
Exhaustive Study ◽  
Fuzzy Histogram

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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