Provably Efficient Multi-Cancer Image Segmentation Based on Multi-Class Fuzzy Entropy
Hend Muslim Jasim, Mudhafar Jalil Jassim Ghrabat, Luqman Qader Abdulrahman, Vincent Omollo Nyangaresi, Junchao Ma, Zaid Ameen Abduljabbar, Iman Qays Abduljaleel
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%.