A Transformer-Based Deep Learning Architecture for Accurate Intracranial Hemorrhage Detection and Classification

Intracranial Hemorrhage (ICH) is a critical medical condition characterized by bleeding within the skull or brain, necessitating rapid and precise diagnosis for optimal treatment and enhanced patient outcomes. This paper introduces an innovative deep learning architecture, specifically the Swin Transformer, for the detection and classification of ICH. The proposed model achieves a remarkable log loss of 0.04372, outperforming traditional convolutional neural network-based approaches. Furthermore, it encompasses a comprehensive desktop application tailored for healthcare professionals, facilitating streamlined ICH assessment. This pioneering approach not only represents a significant advancement in medical imaging but also carries the potential to revolutionize the landscape of ICH diagnosis. The paper aims to bridge the gap between cutting-edge technology and practical healthcare applications, offering invaluable insights that resonate with healthcare practitioners. It is believed that the proposed research findings provide a lucid perspective, empowering healthcare professionals with a powerful tool to enhance their diagnostic capabilities in the critical realm of ICH detection and classification.