Classification of Brain Tumor using Convolutional Neural Networks
Shravya Shetty1, Jyothi Shetty2

1Shravya Shetty, Department of Computer Science and Engineering, NMAM Institute of Technology, Nitte, India.
2Jyothi Shetty, Department of Computer Science and Engineering, NMAM Institute of Technology, Nitte, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2841-2845 | Volume-9 Issue-3, February 2020. | Retrieval Number:  C5995029320/2020©BEIESP | DOI: 10.35940/ijeat.C5995.029320
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Abstract: In medical science, brain tumor is the most common and aggressive disease and is known to be risk factors that have been confirmed by research. A brain tumor is the anomalous development of cell inside the brain. One conventional strategy to separate brain tumors is by reviewing the MRI pictures of the patient’s mind. In this paper, we have designed a Convolutional Neural Network (CNN) to perceive whether the image contains tumor or not. We have designed 5 different CNN and examined each design on the basis of convolution layers, max-pooling, and flattening layers and activation functions. In each design we have made some changes on layers i.e. using different pooling layers in design 2 and 4, using different activation functions in design 2 and 3, and adding more Fully Connected layers in design 5. We examine their results and compare it with other designs. After comparing their results we find a best design out of 5 based on their accuracy. Utilizing our Convolutional neural network, we could accomplish a training accuracy and validation accuracy of design 3 at 100 epochs is 99.99% and 92.34%, best case scenario.
Keywords: Brain tumor classification, Convolutional Neural Networks, magnetic resonance imaging, deep learning, training accuracy, validation accuracy.