Classification of Benign and Malignant MRIs using SVM Classifier for Brain Tumor Detection

Classification of Benign and Malignant MRIs using SVM Classifier for Brain Tumor Detection

© 2022 by IJETT Journal
Volume-70 Issue-2
Year of Publication : 2022
Authors : Ibrahima Sory keita, Ir.Pratap Nair, Haarindra Prasad, Sudhakara pandian, S.Deivasigamani
DOI :  10.14445/22315381/IJETT-V70I2P226

How to Cite?

Ibrahima Sory keita, Ir.Pratap Nair, Haarindra Prasad, Sudhakara pandian, S.Deivasigamani, "Classification of Benign and Malignant MRIs using SVM Classifier for Brain Tumor Detection," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 234-240, 2022. Crossref,

A brain tumor is a clump of malformed tissue in which the cells proliferate rapidly and uncontrollably. Differentiating brain tumors from other brain tissue is critical for clinical diagnosis and therapy methods. This article presented a method for detecting and classifying brain tumor cells using a machine learning algorithm based on the Discrete Wavelet Transform (DWT). Additionally, characteristics from 2D DWT components are retrieved for the categorization of Benign (Be) and Malignant (Ma) Magnetic resonance imaging (MRI). After that, the features are trained and classified using the kernel Support Vector Machine (SVM) classification method. The proposed method stated in this work obtains 99% of accuracy (ACC), 99.14% of sensitivity (Se), and 98.79 % of specificity (Sp) concerning the MRI ground truth images.

Benign and malignant, Brain tumor, DWT, MR images, Neural networks, SVM.

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