Open AccessDetection and classification of brain tumor using machine learning approaches
Author(s) -
B R Pushpa,
Flemin Louies
Publication year - 2019
Publication title -
international journal of research in pharmaceutical sciences
Language(s) - English
Resource type - Journals
ISSN - 0975-7538
DOI - 10.26452/ijrps.v10i3.1442
Subject(s) - computer science , artificial intelligence , support vector machine , pattern recognition (psychology) , construct (python library) , feature extraction , brain tumor , kernel (algebra) , feature (linguistics) , segmentation , key (lock) , machine learning , mathematics , medicine , linguistics , philosophy , computer security , pathology , combinatorics , programming language
This paper proposes a method where a framework is constructed to detect and classify the tumor type. Over a period of years, many researchers have been researched and proposed methods in this space. We have proposed a method that is capable of analyzing the heterogeneous data and classifies tumor type. MRI images have been considered for this project since it gives the clear structure of the brain, without any surgery it scans and gives the structure of the brain this helps in further processing in the detection of the tumor. Human prediction in classifying the tumor from the MRI leads to misclassification. This motivates our project to construct the algorithm to predict the tumor. Machine learning plays a key role in predicting tumor. In this proposed paper, we have constructed a framework for detecting the brain tumor and classifying its type. The approach goes under pre-processing to filter and smooth the image. The segmentation is carried out by using morphological operation followed by masking, which increases the accuracy in the classification step. The multiple feature extraction methods are utilized to extract the feature from the masked image, and for classification, the kernel SVM is used.