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Analysis of the Convolutional Neural Network Model in Detecting Brain Tumor


Affiliations
1 Department of Mathematics Howard University Washington DC, United States
2 Department of Mathematics Defiance College Defiance OH, United States
 

Detecting brain tumors is an active area of research in brain image processing. This paper proposes a methodology to segment and classify brain tumors using magnetic resonance images (MRI). Convolutional Neural Networks (CNN) are one of the effective detection methods and have been employed for tumor segmentation. We optimized the total number of layers and epochs in the model. First, we run the CNN with 1000 epochs to see its best-optimized number. Then we consider six models, increasing the number of layers from one to six. It allows seeing the overfitting according to the number of layers.

Keywords

component; Convolutional Neural Network, Brain Tumor, Data Augmentation.
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Abstract Views: 188

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  • Analysis of the Convolutional Neural Network Model in Detecting Brain Tumor

Abstract Views: 188  |  PDF Views: 96

Authors

Destiny Rankins
Department of Mathematics Howard University Washington DC, United States
Yeona Kang
Department of Mathematics Howard University Washington DC, United States
Dewayne A. Dixon
Department of Mathematics Howard University Washington DC, United States
Seonguk Kim
Department of Mathematics Defiance College Defiance OH, United States

Abstract


Detecting brain tumors is an active area of research in brain image processing. This paper proposes a methodology to segment and classify brain tumors using magnetic resonance images (MRI). Convolutional Neural Networks (CNN) are one of the effective detection methods and have been employed for tumor segmentation. We optimized the total number of layers and epochs in the model. First, we run the CNN with 1000 epochs to see its best-optimized number. Then we consider six models, increasing the number of layers from one to six. It allows seeing the overfitting according to the number of layers.

Keywords


component; Convolutional Neural Network, Brain Tumor, Data Augmentation.

References