Accurate recognition of structural configurations in the cerebral vascular loop, commonly known as the Circle of Willis, is essential for understanding cerebrovascular health, detecting anatomical variations, and predicting neurological risks. Variations in this arterial structure are strongly associated with ischemic stroke, aneurysm formation, and other vascular disorders, making automated and reliable classification methods highly valuable in modern neuroimaging analysis. Traditional approaches rely on manual inspection or purely image-based deep learning techniques, which may fail to capture the complex topological relationships between vessels. To address these limitations, this research proposes a hybrid framework that combines frequency-domain network analysis based on spectral graph theory with deep visual descriptors extracted from convolutional neural networks.

The proposed method models the vascular loop as a graph structure, where arteries are treated as nodes and their connections as edges, enabling the use of spectral analysis to capture global topological properties. Frequency-domain representations derived from graph Laplacian eigenvalues provide structural information that cannot be obtained through spatial image features alone. In parallel, convolutional neural networks are used to extract high-level visual descriptors from magnetic resonance angiography images, capturing local anatomical details. A fusion strategy integrates spectral graph features with deep visual representations to create a unified descriptor capable of identifying anatomical variants with high reliability.

The framework is evaluated using publicly available vascular imaging datasets and challenge benchmarks related to Circle of Willis classification. Experimental results show that combining spectral graph features with deep visual descriptors improves classification accuracy, robustness to noise, and generalization across different imaging conditions compared to single-modality methods. The approach also provides better interpretability because graph-based representations preserve anatomical relationships between vessels.

This study demonstrates that integrating frequency-domain network analysis with deep learning features offers an effective solution for automated recognition of cerebral vascular loop configurations. The proposed method has potential applications in clinical decision support, large-scale screening, and research on cerebrovascular diseases, where accurate structural identification is critical for diagnosis and risk assessment.