SVM Kernels comparison for brain tumor diagnosis using MRI
| Autor: | Wedad Abdul Khuder Naser, Safana Hyder Abbas, Eman Abdulmunem Kadim |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION information science Magnetic resonance imaging Pattern recognition Image processing Thresholding Support vector machine ComputingMethodologies_PATTERNRECOGNITION Kernel (image processing) medicine Preprocessor Brain tumors Kernels Magnetic Resonance Image (MRI) Segmentation Artificial intelligence business |
| DOI: | 10.5281/zenodo.5011609 |
| Popis: | Magnetic Resonance Image (MRI) brain images have an essential role in medical analysis and cancer identification .In this paper multi kernel SVM algorithm is used for MRI brain tumor detection. The proposed work is involving the following stages: image acquisition, image preprocessing, feature extraction and tumor classification. An automatic threshold selection region based segmentation method called Otsu is used for thresholding during preprocessing stage. SVM classification algorithm with four different kernels are used to determine the normal and abnormal images. SVM with quadratic kernel results in best classification accuracy of 86.5%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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