Glioma grade detection using grasshopper optimization algorithm‐optimized machine learning methods: The Cancer Imaging Archive study

Detection of brain tumor's grade is a very important task in treatment plan design which was done using invasive methods such as pathological examination. This examination needs resection procedure and resulted in pain, hemorrhage and infection. The aim of this study is to provide an automated...

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Bibliographic Details
Published in:International journal of imaging systems and technology Vol. 31; no. 3; pp. 1670 - 1677
Main Authors: Hedyehzadeh, Mohammadreza, Maghooli, Keivan, MomenGharibvand, Mohammad
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.09.2021
Wiley Subscription Services, Inc
Subjects:
Algorithms
Brain
Brain cancer
Classification
Feature extraction
fuzzy C‐means
GLCM
glioma grade
grasshopper optimization algorithm
Hemorrhage
Image segmentation
local binary pattern
Machine learning
Magnetic resonance imaging
Medical imaging
Optimization
Optimization algorithms
Support vector machines
Tumors
ISSN:0899-9457, 1098-1098
Online Access:Get full text
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Summary:Detection of brain tumor's grade is a very important task in treatment plan design which was done using invasive methods such as pathological examination. This examination needs resection procedure and resulted in pain, hemorrhage and infection. The aim of this study is to provide an automated non‐invasive method for estimation of brain tumor's grade using Magnetic Resonance Images (MRI). After pre‐processing, using Fuzzy C‐Means (FCM) segmentation method, tumor region was extracted from post‐processed images. In feature extraction, texture, Local Binary Pattern (LBP) and fractal‐based features were extracted using Matlab software. Then using Grasshopper Optimization Algorithm (GOA), parameters of three different classification methods including Random Forest (RF), K‐Nearest Neighbor (KNN) and Support Vector Machine (SVM) were optimized. Finally, performance of three applied classifiers before and after optimization were compared. The results showed that the random forest with accuracy of 99.09% has achieved better performance comparing other classification methods.
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ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22536