Abstract: Intracranial Hemorrhage (ICH) is a serious and potentially fatal condition marked by bleeding in the cranial cavity. It often arises from trauma, high blood pressure, or vascular issues. Early detection of ICH is crucial for improving patient outcomes and lowering mortality rates. Computed Tomography (CT) is the standard method for quickly diagnosing ICH due to its widespread availability and high sensitivity to acute bleeding. Despite this, radiologists must manually interpret CT images, which is labor-intensive and time-consuming, leading to variations in accuracy. Recently, deep learning, especially Convolutional Neural Networks (CNNs), has become a valuable tool for automating medical image analysis. This study looks at how deep learning techniques can automatically detect and classify ICH in brain CT scans. We review existing models, discuss data preprocessing methods, evaluate performance metrics, and highlight commonly used datasets like RSNA and CQ500. We also tackle challenges such as data imbalance, model interpretability, and clinical integration. Our findings show that deep learning models can achieve high diagnostic accuracy and significantly enhance clinical decision-making in emergency situations. Future research should aim to improve model generalization, explainability, and real-time deployment in clinical settings.
Keywords: Intracranial Hemorrhage, CT Scan, Deep Learning, Convolutional Neural Networks, Medical Image Analysis, ICH Detection, Automated Diagnosis, RSNA Dataset, CNN, Healthcare AI
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DOI:
10.17148/IJARCCE.2025.14649
