Enhanced Siamese Network with Multi-Scale Feature Fusion for Precise Ischemic Stroke Analysis and Lesion Characterization
Keywords:
Ischemic stroke, Multi-Scale Feature Fusion, Enhanced Siamese Network, convolutional neural networksAbstract
An ischemic stroke causes cell harm and practical hindrance since it is epitomized by a sudden stoppage of blood stream to a piece of the brain. Exact and early recognition of ischemic stroke lesions is vital for successful treatment arranging. Customary strategies face difficulties because of the intricate and heterogeneous nature of stroke lesions. This paper presents an Enhanced Siamese Network (ESN) with Multi-Scale Feature Fusion (MSFF) pointed toward working on the accuracy of ischemic stroke analysis and lesion characterization. The ESN design influences multi-scale feature extraction and fusion to catch unpredictable lesion subtleties across different scales. The recommended model performs discernibly better than present status of-the-craftsmanship procedures concerning lesion identification exactness and characterization measurements, as per exploratory outcomes on publicly accessible datasets.
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