Optimizing Intracranial Hemorrhage Detection: A Comparative Study of VGG Deep Learning Architectures with Transfer Learning on CT Brain scans

Abstract

Intracranial Hemorrhage (ICH) is a life-threatening condition that requires rapid and accurate diagnosis. This study applied Visual Geometry Group (VGG) deep learning model to binary classification of cerebral hemorrhage in Computerized Tomography (CT) brain scans, thereby improving the diagnostic process. Using a Kaggle dataset containing 6703 images, this study investigated the validity of VGG11, VGG13, VGG16, and VGG19 architectures, using transfer learning of ImageNet pre-trained weights to improve model performance. This study showed that VGG13 with transfer learning outperformed other models with 99.70% accuracy, indicating the profound impact of pre-trained weights on diagnostic accuracy. VGG models with untrained weights performed differently, with VGG11 achieving 98.61% accuracy, suggesting that less complex models were better suited to specific tasks. Comparative analysis shows that the depth of neural networks and the initialization of pre-training weights are the key factors to optimize performance. The discoveries present encouraging possibilities for integrating Artificial Intelligence (AI) into emergency medical services, with the potential to enhance the detection of cerebral hemorrhage and subsequently improve patient outcomes. While acknowledging limitations associated with dataset diversity and model generalization, the outcomes underscore the importance of tailoring models to specific task requirements. These findings also advocate for additional empirical research to fine-tune and advance the application of AI in healthcare.