Open Access

ARTICLE

Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

by Susmita Mishra^1,*, M. Prakash²

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

* Corresponding Author: Susmita Mishra. Email:

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

Received 24 May 2021; Accepted 29 July 2021; Issue published 10 November 2021

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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Citations