Automatic classification and prognosis prediction of cerebral hemorrhage based on a deep learning model

Cerebral hemorrhage presents a major clinical challenge due to its high mortality and complex pathological characteristics. To address the limitations of traditional diagnostic methods, this study proposes HemorrhageNet, a deep learning framework for automatic classification and prognosis prediction of cerebral hemorrhage.

HemorrhageNet integrates multimodal data-including CT and MRI imaging, patient demographics, and clinical parameters-through a dual-path architecture comprising an imaging feature extractor and a clinical feature processor. A graphical propagation layer based on attention mechanisms enables the model to highlight critical hemorrhagic regions, while a multi-task optimization scheme jointly learns classification and prognosis objectives. This design ensures accurate, interpretable, and computationally efficient predictions across diverse patient populations. Building upon this architecture, an adaptive prognostic strategy for cerebral hemorrhage prediction is developed to enhance model generalization and clinical alignment. This strategy incorporates dynamic feature selection to identify the most informative patient-specific attributes, a hierarchical decision-making framework that refines predictions through multi-level reasoning, and uncertainty-aware optimization to quantify confidence and flag ambiguous cases for expert review. These components collectively strengthen interpretability, reduce bias from heterogeneous data, and improve reliability in real-world settings.

Extensive experiments on benchmark medical datasets demonstrate that the proposed framework surpasses existing state-of-the-art methods in accuracy, robustness, and transparency. The integration of HemorrhageNet with the adaptive prognostic strategy provides a comprehensive, explainable solution for cerebral hemorrhage management and prognosis assessment.