Image processing using bipolar fuzzy sets
Abstract
In this article, it is shown that image processing algorithms using bipolar sets and effective recognition results can be achieved through them. Noise and noise in the image are eliminated by means of pre-processing and post-processing algorithms. It has been shown that 95 percent accuracy can be achieved for diagnosis from a given image. In bipolar fuzzy processing, the positive and negative factors affecting the disease were identified and relevance functions were formed. The final software product is produced based on the given algorithm steps. The software consists of separate parts for training and recognition, and in both cases the MRI image processing procedure is the same. In the recognition part, we can obtain recognition results through the processed image recognition model.