Manuscript Number : IJSRST20717
A Review on Brain Tumor Classification Methodologies
Authors(2) :-Bichitra Panda, Dr. Chandra Sekhar Panda Brain tumor is one of the leading disease in the world. So automated identification and classification of tumors are important for diagnosis. Magnetic resonance imaging (MRI)is widely used modality for imaging brain. Brain tumor classification refers to classify the brain MR images as normal or abnormal, benign or malignant, low grade or high grade or types. This paper reviews various techniques used for the classification of brain tumors from MR images. Brain tumor classification can be divided into three phases as preprocessing, feature extraction and classification. As segmentation is not mandatory for classification, hence resides in the first phase. The feature extraction phase also contains feature reduction. DWT is efficient for both preprocessing and feature extraction. Texture analysis based on GLCM gives better features for classification where PCA reduces the feature vector maintaining the accuracy of classification of brain MRI. Shape features are important where segmentation has already been performed. The use of SVM along with appropriate kernel techniques can help in classifying the brain tumors from MRI. High accuracy has been achieved to classify brain MRI as normal or abnormal, benign or malignant and low grade or high grade. But classifying the tumors into more particular types is more challenging.
Bichitra Panda Brain Tumor, DWT, Feature Extraction, Feature Reduction MRI, MRI Classification. Publication Details
Published in : Volume 6 | Issue 6 | November-December 2019 Article Preview
Department of Computer Science and Application, Sambalpur University, Burla, Odisha, India
Dr. Chandra Sekhar Panda
Department of Computer Science and Application, Sambalpur University, Burla, Odisha, India
Date of Publication : 2019-12-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 346-359
Manuscript Number : IJSRST20717
Publisher : Technoscience Academy
Journal URL : https://ijsrst.com/IJSRST20717
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