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Year : 2021  |  Volume : 5  |  Issue : 4  |  Page : 357-365

Identification and categorization of brain tumors using ensemble classifiers with hybrid features

Department of Electronics and Communication Engineering, Sona College of Technology, Salem, Tamil Nadu, India

Correspondence Address:
Royappan Savarimuthu Sabeenian
Department of Electronics and Communication Engineering, Sona College of Technology, Salem - 636 005, Tamil Nadu
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/bbrj.bbrj_163_21

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Day by day the new rate of brain tumor causes is diagnosed. To make use of technology, the standard of lifestyle for many patients was increased their survival rate. Medical imaging modalities were contributing more because the internal formation of the human brain is more complicated to diagnosis. In this field, various modalities are used to detect but the invention of magnetic resonance imaging (MRI) gives more attention, especially to detect brain tumor. The proposed work is focused on segmenting the tumor region in the MR images. The Discrete Wavelet Transform (DWT) is used to extract attributes from tumour images, which is then used with PCA to reduce the dimensionality of the attributes. Gray level co-occurrence method based different features are extracted in the MRI which is given as input to the classification learner such as ensemble, support vector machine, K-nearest neighbor, Naïve, and Fine Tree which are used to classify efficiently. The method is achieved 99% of accuracy according to that trained with 30 images and tested with 100 images. From that tumor images have been split up into benign or malignant. With this trouble-free method is used in MR images to give high accuracy.

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