Brain MRI Classification using Deep Learning Algorithm
Sunita M. Kulkarni1, G. Sundari2
1Sunita M. Kulkarni*, Research Scholar, Sathayabama Institute of Science and Technolgy, Chennai, Assistant Professor, MITWorld Peace University, Pune, India.
2Dr. G. Sundari, ECE Department, Sathayabama Institute of Science and Technolgy, Chennai, India.
Manuscript received on February 06, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on February 30, 2020. | PP: 1226-1231 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C5350029320 /2020©BEIESP | DOI: 10.35940/ijeat.C5350.029320
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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 brain tumor is one of the most dangerous, common and aggressive diseases which leads to a very short life expectancy at the highest grade. Thus, to prevent life from such disease, early recognition, and fast treatment is an essential step. In this approach, MRI images are used to analyze brain abnormalities. The manual investigation of brain tumor classification is a time-consuming task and there might have possibilities of human errors. Hence accurate analysis in a tiny span of time is an essential requirement. In this approach, the automatic brain tumor classification algorithm using a highly accurate Convolutional Neural Network (CNN) algorithm is presented. Initially, the brain part is segmented by thresholding approach followed by a morphological operation. The AlexNet transfer learning network of CNN is used because of the limitation of the brain MRI dataset. The classification layer of Alexnet is replaced by the softmax layer with benign and malignant training images and trained using small weights. The experimental analysis demonstrates that the proposed system achieves the F-measure of 98.44% with low complexity than the state-of-arts method.
Keywords: AlexNet, Brain Tumor classification, MRI, Convolutional neural Network, Deep Learning.