Brain Tumor Classification Using Machine Learning Algorithms

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Brain Tumor Classification Using Machine Learning Algorithms

Journal Title: Elysium Journal of Engineering Research and Management - Year 2016, Vol 4, Issue 2

Abstract

A brain tumor is a collection of tissue that is grouped by a gradual addition of anomalous cells and it is important to classify brain tumors from the magnetic resonance imaging (MRI) for treatment. Human investigation is the routine technique for brain MRI tumor detection and tumors classification. Interpretation of images is based on organized and explicit classification of brain MRI and also various techniques have been proposed. Information identified with anatomical structures and potential abnormal tissues which are noteworthy to treat are given by brain tumor segmentation on MRI, the proposed system uses the adaptive pillar K-means algorithm for successful segmentation and the classification methodology is done by the two-tier classification approach. In the proposed system, at first the self-organizing map neural network trains the features extracted from the discrete wavelet transform blend wavelets and the resultant filter factors are consequently trained by the K-nearest neighbour and the testing process is also accomplished in two stages. The proposed two-tier classification system classifies the brain tumors in double training process which gives preferable performance over the traditional classification method. The classifiers can accurately classifying the status of the brain image into normal / abnormal.

Authors and Affiliations

Balakumar B. , Divya Devi E. , Raviraj P.

Keywords

Image segmentation MRI (Magnetic Resonance Imaging) Adaptive pillar k- means algorithm

EP ID EP365662
DOI -
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