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Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

Susmita Mishra1,*, M. Prakash2

1 Department of CSE, Rajalakshmi Engineering College, Chennai, India
2 Karpagam College of Engineering, Coimbatore, India

* Corresponding Author: Susmita Mishra. Email: email

Computer Systems Science and Engineering 2022, 41(3), 1099-1115. https://doi.org/10.32604/csse.2022.020439

Abstract

In the medical field, the detection of breast cancer may be a mysterious task. Physicians must deduce a conclusion from a significantly vague knowledge base. A mammogram can offer early diagnosis at a low cost if the breasts' satisfactory mammogram images are analyzed. A multi-decision Intuitionistic Fuzzy Evidential Reasoning (IFER) approach is introduced in this paper to deal with imprecise mammogram classification efficiently. The proposed IFER approach combines intuitionistic trapezoidal fuzzy numbers and inclusion measures to improve representation and reasoning accuracy. The results of the proposed technique are approved through simulation. The simulation is created utilizing MATLAB software. The screening results are classified and finally grouped into three categories: normal, malignant, and benign. Simulation results show that this IFER method performs classification with accuracy almost 95% compared to the already existing algorithms. The IFER mammography provides high accuracy in providing early diagnosis, and it is a convenient diagnostic tool for physicians.

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APA Style
Mishra, S., Prakash, M. (2022). Digital mammogram inferencing system using intuitionistic fuzzy theory. Computer Systems Science and Engineering, 41(3), 1099-1115. https://doi.org/10.32604/csse.2022.020439
Vancouver Style
Mishra S, Prakash M. Digital mammogram inferencing system using intuitionistic fuzzy theory. Comput Syst Sci Eng. 2022;41(3):1099-1115 https://doi.org/10.32604/csse.2022.020439
IEEE Style
S. Mishra and M. Prakash, "Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory," Comput. Syst. Sci. Eng., vol. 41, no. 3, pp. 1099-1115. 2022. https://doi.org/10.32604/csse.2022.020439

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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