3D Brain Tumor Detection Using 3D CNNs and Graph Convolutional Networks (GCNs)
Year: 2025
Type: article
Abstract: This study introduces a novel hybrid approach for accurate 3D brain tumor detection using advanced deep learning techniques. By integrating 3D Convolutional Neural Networks (3D CNNs) and Graph Convolutional Networks (GCNs), the research captures both spatial features and structural relationships within volumetric brain scans. The model, trained on MRI and CT datasets, achieves a high accuracy of 99.4% in testing. It leverages transfer learning, supervised validation, and data augmentation to enhance precision and generalization. The paper also emphasizes clinical relevance, post-processing visualization, and ethical deployment, paving the way for real-time, AI-assisted diagnostics in neuro-oncology.
Source: Figshare
Authors: Eman, Alishba, Osama Ali Shah, Syed
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6902256472
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