Brain tumor types and grades classification based on statistical feature set using support vector machine

Priya, K.; Kavitha, S.; Bharathi, B.

doi:10.1109/isco.2016.7726910

Cited by 24 publications

(8 citation statements)

References 7 publications

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“…Mohan priya et al(2014) [18] detect the brain tumor using multi class SVM and they train their system with image features. They divide image features in two categories, first order and second order features.…”

Section: Related Workmentioning

confidence: 99%

“…Here image intensity values are i and j, spatial position of an image is X and Y in the image Img and the offset (Δx, Δy) rely on the way used and the distance at which the matrix is computed [18] GLCM technique is use to extract textural features of an image in the above equation we use following notaions;- [19] G:-number of gray levels µ:-mean value of p µx, µy:-means of Px and Py σx , σy:-Standard Deviation The following features are used:…”

Section: A Feature Extractionmentioning

confidence: 99%

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A Novel Approach for the Extraction and Classification of Tumor in MR Images of the Brain via Principle Component Analysis and Kernel Support Vector Machine

Pareek¹

2017

ijarcs

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Medical imaging plays an important role in the medical field. In present time there are various computerized methods for medical imaging to diagnose the inner portion of human body. Brain tumor detection is an important application in recent days. In this paper, various methods have been used for brain tumor detection and classification taking magnetic resonance images as input. For tumor classification, we had done experimentation with 50 MRI images taken from "figshare brain data set". We propose an efficient method for brain tumor classification to classify cancerous and noncancerous tumor. The proposed method has three major steps 1.) Feature extraction 2) Feature reduction and 3) Classification. The present approach extracts the statistical texture features using 2D Discrete Wavelet transformation (Daubechies) and GLCM. PCA (principle Component Analysis) is used for feature reduction. We create a training data set that was carried out with 50 MRI images and applied SVM classifier to classify the tumor whether it is Benign and Malignant and evaluate the performance by Kernel based SVM.

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Section: Related Workmentioning

confidence: 99%

Section: A Feature Extractionmentioning

confidence: 99%

A Novel Approach for the Extraction and Classification of Tumor in MR Images of the Brain via Principle Component Analysis and Kernel Support Vector Machine

Pareek¹

2017

ijarcs

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“…P ΔxΔy (X,Y) = (2) Here image intensity values are i and j, the spatial position of an image is X and Y in the image ,Img and the offset (Δx, Δy) rely on the way used and the distance at which the matrix is computed. [9] GLCM technique is used to extract textural features of an image in the above equation we use following notations;- [10] G:-number of gray levels µ:-mean value of p µx, µy:-means of Px and Py σx , σy:-Standard Deviation…”

Section: The First-order Histogram P(i) Is Defined Asmentioning

confidence: 99%

Classification of Brain Tumor using GLCM and SVM from MRI Images

2017

Dec. 7-8, 2017 Paris (France) CCCME-17, MAIE-17, CAEBS-17, L2hss-17 &Amp; LEBHM-17

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“…This study obtained standard brain accuracy and brain tumor identification of 98.00%. Classification of brain tumors based on the statistical feature set uses the support vector machine with an accuracy of 68.1% [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit

Fahmi

Apriyulida

Nasution

et al. 2020

Journal of Healthcare Engineering

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Patients in the intensive care unit require fast and efficient handling, including in-diagnosis service. The objectives of this study are to produce a computer-aided system so that it can help radiologists to classify the types of brain tumors suffered by patients quickly and accurately; to build applications that can determine the location of brain tumors from CT scan images; and to get the results of the analysis of the system design. The combination of the zoning algorithm with Learning Vector Quantization can increase the speed of computing and can classify normal and abnormal brains with an average accuracy of 85%.

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Brain tumor types and grades classification based on statistical feature set using support vector machine

Cited by 24 publications

References 7 publications

A Novel Approach for the Extraction and Classification of Tumor in MR Images of the Brain via Principle Component Analysis and Kernel Support Vector Machine

A Novel Approach for the Extraction and Classification of Tumor in MR Images of the Brain via Principle Component Analysis and Kernel Support Vector Machine

Classification of Brain Tumor using GLCM and SVM from MRI Images

Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit

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