Prediction of Brain Hemorrhage Using Deep Learning Techniques

Abstract

Brain hemorrhage is a severe and life-threatening condition where timely and correct diagnosis can make the cleary difference between survival damage and permanent damage. Conventional diagnostic methods, such as MRI and CT scans evaluated by radiologists, are often limited by delays in interpretation and inconsistencies between observers. To overcome these challenges, this study introduces an automated deep learning framework capable of detecting and classifying different subtypes of brain hemorrhage directly from MRI images. The proposed approach integrates a modified ResNet-50 network, enhanced with Squeeze-and-Excitation (SE) blocks for better feature selection, along with a U-Net–based segmentation module to accurately localize critical brain regions. Training was carried out on the RSNA Intracranial Hemorrhage Detection dataset, supported by transfer learning and extensive data augmentation to strengthen model generalization. Experimental results highlight that the system delivers considerable performance gains compared to baseline models, with an overall accuracy of 94.6%, an F1-score of 93.0%, and an AUC-ROC of 95.4%. These outcomes demonstrate the effectiveness of combining spatial attention mechanisms with domain-specific preprocessing to improve both diagnostic accuracy and clinical interpretability. The framework shows potential for real-time application in emergency settings and lays the groundwork for future AI-based diagnostic tools in neuroimaging.