Fuzzy C Means & Genetic Algorithm for MRI-based Brain Tumor Identification

Registration ID: IJNRD_197017

Published ID: IJNRD2305833

DOI: Click Here to Get

Subhashini K , Dr Thangakumar J

Optimization, Magnetic Resonance, Fuzzy C Means, Genetic Algorithm, K-Means, PSO

The method of segmenting pictures of brain tumors is an important part of the medical sector and the processing of medical information. Early diagnosis of brain tumors in patients is the single most important factor in determining the patient's prognosis and treatment options. Finding brain tumors at an earlier stage will improve patients' chances of living longer. Due to the need of automated image fragmentation, a neurologist will commonly employ a physical image classification, which is a method that is challenging and time-consuming. In this research, we discuss various optimization-based proposed that perceived that may be used to identify brain tumors in images obtained from magnetic resonance (MR) scanners. Throughout this investigation, an evaluation of recently reported research is carried out, as well as an attempt is made to develop a brand-new model based on Fuzzy C Means as well as Genetic Algorithm (FCMGA), with both the intention of automatically identifying and classifying brain tumors in MRI images. The objective of the study is to achieve this objective. The modeling was performed between different indicators that analyze the effectiveness of the classification methods, such as K-Means and FCM, as well as some of the hybrid techniques for optimized fragmentation, such as clustering accompanied by Genetic Algorithm (GA), as well as clustering with Particle Swarm Optimization. The above classification methods involve K-Means and FCM (PSO). In order to perform these categorization procedures, first the MRI image must be pre-processed, and then the additional classification or improvement methods must be used in order to get a tumor that is more distinct and simpler to identify. The outcomes of the survey indicate that the model that was developed may be useful in providing accurate detection of brain tumors.

Published Paper   E-Certificate

Subhashini K & Dr Thangakumar J (May-2023). Fuzzy C Means & Genetic Algorithm for MRI-based Brain Tumor Identification. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), i248-i253. https://ijnrd.org/papers/IJNRD2305833.pdf

Volume 8 Issue 5, May-2023

Pages : i248-i253

Paper Reg. ID: IJNRD_197017

Published Paper Id: IJNRD2305833

Downloads: 000121974

Research Area: Engineering

Country: Chennai/Chengalpattu , Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305833.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305833

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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