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About Accepted Manuscript
This paper proposes a methodology using deep neural networks (DNN) for brain tumor segmentation and classification using magnetic resonance images (MRI). The proposed model uses 7 layers consisting of 3 convolutional layers, 3 ReLU layers, and a softmax layer. The model takes MRI image patches as input and assigns labels to classify tumors. The methodology is tested on 8 benchmark brain tumor datasets and results are validated using various metrics such as accuracy, sensitivity, specificity, dice similarity coefficient, precision, and others. The paper finds that DNN is able to accurately segment and classify brain tumors in MRI images.
Detailed Information
| Author: | ['Zeeshan'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 20 |
| Language: | English |
| Format: | |
| Price: | FREE |
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