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Title |
Enhanced CNN Model for Brain Tumor Classification |
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Author |
Aravinda Kasukurthi, Lakshmikanth Paleti, Madamanchi Brahmaiah , Ch.Sudha Sree |
| Citation |
Vol. 22 No. 5 pp. 143-148 |
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Abstract |
Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent. |
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Keywords |
Multi-classification, CNN model, Grid search technic, Hyper parameter optimization |
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