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

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  • Chiranji Lal Chowdhary

    (School of Information Technology and Engineering, VIT University, Vellore, India)

  • D. P. Acharjya

    (School of Computer Science and Engineering, VIT University, Vellore, India)

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 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.

Suggested Citation

  • Chiranji Lal Chowdhary & D. P. Acharjya, 2016. "A Hybrid Scheme for Breast Cancer Detection using Intuitionistic Fuzzy Rough Set Technique," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 11(2), pages 38-61, April.
    • Handle: RePEc:igg:jhisi0:v:11:y:2016:i:2:p:38-61
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    Cited by:

    1. Vijayalaxmi Mekali & Girijamma H. A., 2021. "Fully Automatic Detection and Segmentation Approach for Juxta-Pleural Nodules From CT Images," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(2), pages 87-104, April.

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