Intracranial Hemorrhage Diagnosis Using Deep Learning: A Survey of Techniques, Frameworks, and Challenges

Abstract

Intracranial hemorrhage (ICH) is a life-threatening medical emergency that demands rapid and accurate diagnosis to improve survival and clinical outcomes. With advancements in artificial intelligence (AI), particularly deep learning, significant progress has been made in automating ICH detection, segmentation, and classification using medical imaging data, especially CT scans. This survey presents a detailed analysis of various deep learning architectures—such as CNNs, RNNs, U-Net, and hybrid ensemble methods—applied in ICH research. We review key segmentation and classification models, discuss optimization strategies, explore the role of synthetic data and augmentation, and emphasize the increasing importance of explainable AI in clinical practice. Publicly available datasets and evaluation metrics are also examined to highlight the current landscape. The paper concludes by identifying persistent challenges and proposing future directions for research to enhance clinical applicability of AI-based ICH diagnosis systems.