Brain MR image tumor detection and classification using neuro fuzzy with binary cuckoo search technique

Abstract

Brain tumor and stroke are two important causes of death in and around the world. Tumor classification and retrieval system plays a vital role in medical field. Tumor detection, segmentation and MR imaging seizures are a major concern, although it can be a daunting and tedious task for clinical specialists, the accuracy of which depends solely on their experience. In this article, the neuro fuzzy with binary cuckoo search optimization method is proposed for detecting tumors on MR images. The method has four stages. In the first step, raw MR images are pre-processed by the anisotropic filter, and in the second phase, the removal of the skull is classified by type. The third phase involves the functioning of singular value decomposition and principle component analysis. Finally, the NFBCS method is used to detect and classify tumors and the BCS algorithm optimizes the study model for better classification accuracy.

A Systematic Review of Brain MRI Segmentation and Uncertainty Modeling Using Evidence Theory with Implementation of Fuzzy Clustering and Fuzzy Inference Systems Methods

“…To achieve satisfactory results in brain MRI segmentation, a method capable of dealing with uncertainty is necessary. Non-phased methods relied on pre-processing, but due to the variety of MRI problems and the complexity of modeling error sources in MRI, these methods may not always produce the desired results [7]. Alternatively, researchers have developed fuzzy methods that can model and manage uncertainty.…”