BackgroundCerebral infarction remains a leading cause of mortality and long-term disability worldwide, demanding rapid and accurate diagnostic strategies. However, current assessments primarily rely on imaging interpretation, often neglecting valuable clinical and laboratory information that could enhance diagnostic precision.MethodsWe developed NeuroFusionNet, a multi-modal deep learning framework that integrates imaging features with clinical biomarkers for binary classification of cerebral infarction and healthy controls. The model combines a ResNet-based visual encoder with a multilayer perceptron branch for clinical indicators, achieving end-to-end feature fusion and joint optimization.ResultsNeuroFusionNet achieved superior diagnostic performance with an accuracy of 0.9655, precision of 0.9584, recall of 0.9584, and F1-score of 0.9584, significantly outperforming baseline models including ResNet, MobileNet, and GhostNet. The integration of imaging and clinical biomarkers effectively enhanced model sensitivity and robustness, demonstrating strong potential for real-world clinical application.ConclusionOur findings highlight the clinical value of integrating imaging and laboratory data for precision diagnosis of cerebral infarction. NeuroFusionNet provides a scalable and interpretable framework that may support early detection and personalized management of cerebrovascular diseases in routine clinical practice.
Radically different