Application of Machine Learning and Deep Learning Methods for Brain Tumor Identification and Classification
Hetal Barad1, Atul Patelx2
1Hetal Barad, Research Scholar, FCA – Faculty of Computer Science and Applications, Charotar University of Science and Technology, Changa, India.
2Atul Patel*, FCA – Faculty of Computer Science and Applications, Charotar University of Science and Technology, Changa, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 2886-2891 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8062038620/2020©BEIESP | DOI: 10.35940/ijrte.F8062.038620
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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: In the area of medical imaging technology, advances in Artificial intelligence (AI) delivers promising solutions with higher accuracy. For healthcare solutions, medical images provides a systematic way for diagnosis the diseases earlier and make treatments more effective. Machine learning and deep learning are rapidly grown fields of AI that may apply to many domains including image processing, speech recognition and text understanding. As MRI image segmentation is a key task for identification of brain anomalies, a fast and reliable technique is essential for increasing the survival ratio of affected patients. Manual segmentation of the brain MRI image involves more time and it may subject to inaccuracies. Hence, AI approaches and algorithms have been developed for tumor segmentation. This paper contains the detailed study of the available methods of machine learning and deep learning for brain tumor identification and classification through MRI image segmentation. It discusses and summarizes the methodologies and its results available for classification of brain tumor.
Keywords: Classification, Convolutional Neural Network, Image Processing.
Scope of the Article: Neural Information Processing.