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

Author

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  • Shler Farhad Khorshid

    (Information Technology Department, Akre Technical College of Informatics, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq)

  • Nawzat Sadiq Ahmed

    (Information Technology Management, Technical College of Administration, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq)

Abstract

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.

Suggested Citation

  • 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, IJSAB International, vol. 5(6), pages 12-20.
    • Handle: RePEc:aif:journl:v:5:y:2021:i:6:p:12-20
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    References listed on IDEAS

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    1. Nasiba Mahdi Abdulkareem & Adnan Mohsin Abdulazeez, 2021. "Machine Learning Classification Based on Radom Forest Algorithm: A Review," International Journal of Science and Business, IJSAB International, vol. 5(2), pages 128-142.
    2. Nareen O. M. Salim & Adnan Mohsin Abdulazeez, 2021. "Human Diseases Detection Based On Machine Learning Algorithms: A Review," International Journal of Science and Business, IJSAB International, vol. 5(2), pages 102-113.
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    Cited by:

    1. Noor Salah Hassan & Nawzat Sadiq Ahmed, 2021. "A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 21-32.

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    1. Noor Salah Hassan & Nawzat Sadiq Ahmed, 2021. "A Comparative Study of Detect Brain Tumor Based on K-Means and Fuzzy C-Means Algorithms," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 21-32.

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