SmartSEO: Head Hemorrhage Using Deep Learning

Abstract

Early and accurate detection of head haemorrhage is a critical requirement in the field of medical diagnostics, as delayed identification can lead to severe neurological complications or even mortality. Conventional diagnostic procedures rely heavily on manual interpretation of computed tomography (CT) or magnetic resonance imaging (MRI) scans by radiologists, which can be time-consuming and prone to human error, especially in emergency scenarios. To overcome these limitations, an intelligent deep learning-based system has been developed to automatically detect and classify head haemorrhages from medical images or clinical text reports. The proposed system integrates advanced convolutional neural networks (CNNs) and natural language processing (NLP) techniques to analyse multimodal data effectively. Initially, input images undergo preprocessing steps such as noise removal, normalisation, and contrast enhancement to improve feature visibility. The CNN model then extracts spatial features to identify haemorrhage regions, while NLP modules like Sentence-BERT or MiniLM process textual diagnostic reports to cross-validate findings. The processed outputs are compared against trained classification layers to determine the presence and type of haemorrhage, such as epidural, subdural, intracerebral, or subarachnoid. The system achieves high accuracy and robustness through training on large-scale annotated datasets and performance optimisation using metrics like precision, recall, and F1score. Visualisation components such as heatmaps assist clinicians by highlighting affected regions, enhancing interpretability and trust. Designed with scalability and adaptability in mind, the model can be integrated into hospital information systems for real-time diagnosis assistance. Experimental results demonstrate that the proposed approach significantly reduces diagnosis time while maintaining high reliability, contributing to improved patient outcomes and clinical decision support. This innovation offers a fast, intelligent, and precise alternative to conventional diagnostic methods in neuroimaging and medical informatics