Diagnosis and Classification of Brain Tumor
Ravendra Singh1, Navin Prakash2
1Ravendra Singh*, Research Scholar Rajiv Gandhi Prodhyogiki Vishwavidyalaya, Department of Computer Science and Engineering University Bhopal (MP) india.
2Dr Navin Prakash, Rajiv Gandhi Prodhyogiki Vishwavidyalaya, Department of Computer Science and Engineering University Bhopal (MP) india.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1622-1630 | Volume-9 Issue-1, October 2019 | Retrieval Number: F8767088619/2019©BEIESP | DOI: 10.35940/ijeat.F8767.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The aim of this paper is to provide an outline on cerebrum (brain) tumor diagnoses for folks that are new to virtual medical image processing. The strange improvement of cells within the brain is brain Tumor. Detection of brain tumor at early stage is feasible with image processing and the development of device learning. For early detection of extraordinary adjustments in tumor cells, Computer tomography, Magnetic resonance imaging strategies are used. Early detection and identification of tumor is the handiest manner to get remedy. Brain tumor is classed into two kinds benign and malignant tumor. Various imaging processing strategies had been proposed in latest couple of years for detection and class of brain tumor. Automatic segmentation method the usage of clustering and convolution neural community gives nice consequences. The PCA has been used for reducing the features from the segmented area gives superb outcomes compared to other techniques. For classification of brain tumors diverse algorithms any such Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor are reviewed. These strategies correctly paintings on CT and MRI images
Keywords: Computer tomography (CT), magnetic resonance imaging (MRI), Principal Component Analysis (PCA), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN).