Modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Brain tumor is an abnormal mass of tissue in which some cells grow and multiply uncontrollably, apparently unregulated by the mechanisms that control normal cells. There are three types of tumor that are commonly observed viz. Benign, Pre-Malignant, and Malignant. Many supervised and unsupervised classification algorithms are used for detection of tumor as benign or malignant. Usually lighter datasets are used for image classification in application field where as comparatively larger and heavier datasets are used in case of medical field. Many parameters chosen during training play a very important role in measuring the performance and accuracy of the system. Thus an attempt has been made to clearly show how accuracy of the algorithm varies based on the parameters chosen for detection of brain tumor in human brain for an MRI image.

CNN, Transfer Learning, Medical Imaging, Glioma, Image Classification, Machine Learning, Deep Learning

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