Neutrosophic Clustering: A Solution for Handling Indeterminacy in Medical Image Analysis

Abstract

The need for additional innovation in the healthcare industry has become more apparent as the world begins to recover from the ravages of the pandemic. While computational intelligence has quietly become integrated into more and more fields, its applications were not something the average person discussed until recently. Computational Intelligence is becoming more and more applicable in several sectors around the world like health, industrial, business and commercial sectors. A.I.’s ability to provide faster and improved functionality is what healthcare workers at healthcare centers believe will be a significant implication in the strife towards improving healthcare delivery and patient care. One of the major applications of A.I. in healthcare is pattern mapping of medical images which mainly involves image processing. It seeks to extract significant things from the image through clustering. Therefore, choosing a suitable clustering method for a specific data set is a crucial step in the process of image segmentation. Numerous modifications to the clustering algorithm, such as the fuzzy k-mean algorithm, have been presented up to this point. All of the data mining techniques currently in use are capable of handling the uncertainty brought on by numerical deviations or unpredictable phenomena in the natural world. But, present data mining challenges in the real world may include indeterminacy components. In this article, we propose a new clustering approach for the segmentation of dental X-ray images that is based on neutrosophic logic. The authentic dental patients’ dataset from KIDS(Kalinga Institute of Dental Science) Hospital is used to validate the proposed approach. The experimental findings demonstrated the proposed method’s superiority in terms of clustering quality over the existing ones.