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Brain MR image tumor detection and classification using neuro fuzzy with binary cuckoo search technique
Author(s) -
Arumugam Selvapandian,
Paulraj Sivakumar,
Selvaraj Nagendra Prabhu
Publication year - 2021
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22550
Subject(s) - cuckoo search , artificial intelligence , computer science , pattern recognition (psychology) , cuckoo , binary number , image (mathematics) , fuzzy logic , mathematics , machine learning , biology , arithmetic , zoology , particle swarm optimization
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.