Recognizing Intracranial Hemorrhage in CT Images by Deep Learning Models

Abstract

Intracranial hemorrhage (ICH) is a life-threatening neurological emergency that requires rapid and accurate diagnosis to improve patient outcomes. While CT imaging is the clinical standard for ICH detection, manual interpretation by radiologists is often time-consuming and prone to variability. In recent years, artificial intelligence (AI) has shown great potential in automating this diagnostic process. This paper presents a comprehensive review of AI-based methods for ICH detection, focusing on three major algorithmic approaches: artificial neural networks (ANNs), convolutional neural networks (CNNs), and vision transformers (ViTs). ANN models provide early insights through manually designed features, while 3D CNNs significantly improve spatial understanding of bleeding areas through end-to-end learning. ViT-based models further advance the field by leveraging global attention mechanisms to capture long-range dependencies between CT slices. This paper highlights key innovations across various categories, discusses the clinical relevance of recent architectures, and identifies challenges currently faced by AI detection, including lack of interpretability, domain generalization capabilities, and patient data privacy issues. Through a modular comparison of representative studies, this research provides valuable insights into the development and deployment of artificial intelligence in neuroimaging tasks. By combining technological advancements with practical considerations, this review aims to provide resources for researchers and clinicians and offer a timely and comprehensive perspective on the ongoing development of AI in medical diagnostics.