A comparison study: Classification brain tumor based on Support Vector Machine and K-Nearest Neighbors
Shler Farhad Khorshid & Nawzat Sadiq Ahmed
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.
|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|
|ISSN:||ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)|
|Date of Publication:||09/02/2021|
|Paper Type:||Literature review|
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
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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.email@example.com
Nawzat Sadiq Ahmed, Information Technology Management, Technical College of Administration, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq. Email: firstname.lastname@example.org