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Brain-GCN-Net: Graph-Convolutional Neural Network for brain tumor identification
1Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, Adana, 01250, Turkey.
Computers in Biology and Medicine
|August 6, 2024
Summary
A novel fused deep learning model combining Graph Neural Networks and Convolutional Neural Networks significantly improves brain tumor classification accuracy. This AI approach enhances detection by analyzing both relational dependencies and spatial features in medical images.
Area of Science:
- Artificial Intelligence
- Medical Image Analysis
- Deep Learning
Background:
- AI and medical imaging are revolutionizing brain tumor detection and diagnosis.
- Accurate detection and classification are vital for early diagnosis and treatment.
- Traditional Convolutional Neural Network (CNN) models struggle with complex medical image features.
Purpose of the Study:
- To propose a fused Deep Learning (DL) model integrating Graph Neural Networks (GNN) and CNN for improved brain tumor detection.
- To enhance the comprehensive representation of brain tumor images and boost classification performance.
Main Methods:
- A fused DL model combining GNNs for relational dependencies and CNNs for spatial features was developed.
- The model was evaluated on a public dataset comprising 10,847 MRI images.
- Performance was compared against existing pre-trained models and traditional CNN architectures.
Main Results:
- The fused DL model achieved 93.68% accuracy in brain tumor classification.
- The proposed model demonstrated superior performance compared to existing pre-trained and traditional CNN models.
- This indicates the effectiveness of integrating GNNs and CNNs for this task.
Conclusions:
- The developed fused DL model shows significant potential for brain tumor classification.
- Further investigation in clinical trials is recommended to assess its utility in improving clinical decision-making.
