Preoperative Grading of Brain Gliomas Using 3D-ResNet18 Based on Multimodal MRI and Attention Mechanism
DOI:
https://doi.org/10.12974/2313-1047.2024.11.02Keywords:
Glioma classification, 3D-residual networks, Magnetic resonance imaging, Deep learning, Multimodal magnetic resonance imagingAbstract
In response to the prevalent challenge of imprecise preoperative glioma grading prediction, a multimodal Magnetic Resonance Imaging (MRI) image with attention mechanism prediction model based on Residual Network (ResNet) is proposed for accurate prediction. To achieve this, publicly accessible datasets from BraTS 2019 and BraTS 2020 were employed. MRI images across three modalities underwent preprocessing, cropping, and subsequent stacking to generate comprehensive multimodal representations. Meanwhile, An Efficient Channel Attention (ECA) mechanism based on 3D data (3D-ECA) is proposed and introduced to reduce the problem of slow network convergence and overfitting. Notably, experiments demonstrate that the 3D-ECA attention mechanism added to the model improves the network training speed and the classification accuracy of the model, and the classification accuracy reached 88.1%, with an impressive area under the subject working characteristic curve of 0.918. Therefore, the 3D-ResNet18 deep learning model, incorporating multimodal MRI and 3D-ECA attention mechanisms, demonstrates high accuracy and robustness in distinguishing between high-grade and low-grade gliomas. Thus, showcasing significant clinical potential for glioma classification and diagnosis.
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