Abstract:
Intracranial hemorrhage (ICH) is a life-threatening condition that requires a timely
and accurate prognosis to guide critical clinical decisions. Traditionally, radiologists
and clinicians assess prognosis through the evaluation of CT scans and patient
history; however, time constraints and inter-observer variability can limit this
manual process. In this study, convolutional neural networks (CNNs), including
ResNet-18, DenseNet-121, and VGG-16, were employed to analyze CT scan slices.
At the same time, tree-based machine learning models random forest and extreme
gradient boosting (XGBoost) were used to process clinical tabular data. To improve
transparency and trust in model predictions, explainable AI (XAI) methods
SHapley Additive exPlanations (SHAP) for clinical data and Gradient-weighted
Class Activation Mapping (Grad-CAM) for CT images were applied. The ResNet-
18 model achieved the highest performance among image-based models, while the
random forest model with recursive feature elimination (RFE) led among tabular
models. A web application was developed to enable clinicians to upload CT scans
and clinical data, receiving prognosis predictions along with visual explanations,
thereby serving as an accessible decision-support tool in clinical settings.
