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A comparison study: Classification brain tumor based on Support Vector Machine and K-Nearest Neighbors Shler Farhad Khorshid & Nawzat Sadiq Ahmed

A comparison study: Classification brain tumor based on Support Vector Machine and K-Nearest Neighbors

Author (s)

Shler Farhad Khorshid & Nawzat Sadiq Ahmed

Abstract

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Brain tumor is one of the commonest tumors. For the diagnosis of this disease, automated detection and classification are crucial. Magnetic resonance imaging (MRI) is a unique sort of imaging which is utilized for detecting these tumors and categorizing them as benign or malignant using special algorithms such as of K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). The classification of brain tumors through imaging can be divided into four phases: pre-processing, extraction, segmentation and classification. This paper reviews some recent studies that highlight the efficacy of K-NN and SVM accuracies in classifying brain MRI images as normal or abnormal, benign or malignant.

 Keywords: brain tumor, Magnetic resonance imaging (MRI), classification, SVM, K-NN.

 

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Title:A comparison study: Classification brain tumor based on Support Vector Machine and K-Nearest Neighbors
Author:Shler Farhad Khorshid & Nawzat Sadiq Ahmed
Journal Name:International Journal of Science and Business
Website:ijsab.com
ISSN:ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI:https://doi.org/10.5281/zenodo.4590054
Media:Online
Volume:5
Issue:6
Acceptance Date:05/03/2021
Date of Publication:09/02/2021
PDF URL:https://ijsab.com/wp-content/uploads/742.pdf
Free download:Available
Page:12-20
First Page:12
Last Page:20
Paper Type:Literature review
Current Status:Published

 

Cite This Article:

Shler Farhad Khorshid & Nawzat Sadiq Ahmed (2021). A comparison study: Classification brain tumor based on Support Vector Machine and K-Nearest Neighbors. International Journal of Science and Business, 5(6), 12-20. doi: https://doi.org/10.5281/zenodo.4590054

Retrieved from https://ijsab.com/wp-content/uploads/742.pdf

 

About Author (s)

Shler Farhad Khorshid (corresponding author), Information Technology Department, Akre Technical College of Informatics, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. E-mail: Shler.sulayvani@gmail.com

Nawzat Sadiq Ahmed, Information Technology Management, Technical College of Administration, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. Email: nawzat.ahmed@dpu.edu.krd

 

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DOI: https://doi.org/10.5281/zenodo.4590054