Cancer Disease Treatment Classification Using Extended Rough based Intuitionistic Fuzzy C-means Learning Methodology

Abstract

Because of the sensitivity of magnetic resonance to noise for the diagnosis and study of cancer, classifying magnetic resonance disease treatments is a medically complex and vitally important process. To sum up, these are the most pressing problems with the current system for describing how to treat cancer. Mathematical methods like rough sets, fuzzy sets, and rough sets are utilized to assess and cope with the ambiguity and uncertainty present in medical cancer disease treatments. Rough sets and rough sets-based techniques have both been offered in past, but depending on the parameters, each has its own special set of issues. The Extended Rough based Intuitionistic Fuzzy C-means Learning Technique (ERIFCM) with estimation of weight bias parameter for Cancer Disease Treatment Classification presented in this work is a novel method for computing the disease treatment classification. Using the non-membership and membership values in intuitionistic Rough based fuzzy sets, a generalised kind of fuzzy, rough sets and their representative components are evaluated. The approach that is proposed in this research uses existing clustering's standard features to lower the intensity and noise associated with cancer treatments without the need for spatial weight context data. In addition, the cluster centroid is initialized using the max-dist evaluation method, which is based on the weight measure, before the proposed algorithm is executed. This helps to reduce the number of iterations required for clustering. In contrast to existing segmented approaches developed for Cancer Disease Treatment and similar Disease Treatments, experimental results of the proposed approach yielded effective Disease Treatment Classification results. The main component of the proposed method is a more thorough analysis of experimental results.