Provably Efficient Multi-Cancer Image Segmentation Based on Multi-Class Fuzzy Entropy

Abstract

One of the segmentation techniques with the greatest degree of success used in numerous recent applications is multi-level thresholding. The selection of appropriate threshold values presents difficulties for traditional methods, however, and, as a result, techniques have been developed to address these difficulties multidimensionally. Such approaches have been shown to be an efficient way of identifying the areas affected in multi-cancer cases in order to define the treatment area. Multi-cancer methods that facilitate a certain degree of competence are thus required. This study tested storing MRI brain scans in a multidimensional image database, which is a significant departure from past studies, as a way to improve the efficacy, efficiency, and sensitivity of cancer detection.  The evaluation findings offered success rates for cancer diagnoses of 99.08%, 99.87%, 94%; 97.08%, 98.3%, and 93.38% sensitivity; the success rates of the LED Internet connection in particular were 99.99%; 98.23%, 99.53%, and 99.98%.