Integration of imaging and clinical biomarkers for cerebral infarction diagnosis via NeuroFusionNet.

Background: Cerebral 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. Methods: We 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. Results: NeuroFusionNet 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. Conclusion: Our 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.