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.
