Deep Learning Approaches for Multilabel Classification of Brain Hemorrhages in CT Imaging

As a serious neurological emergency, intracranial hemorrhage requires an early and precise diagnosis to improve the patient's chances of recovery. Using the RSNA Intracranial Hemorrhage Challenge dataset, this study introduces a deep learning system for the automated detection and categorization of brain hemorrhages from computed tomography (CT) scans. To improve the data and strengthen the model, preprocessing methods such as contrast improvement, scaling, augmentation, and intensity normalization were used. There were five different kinds of hemorrhages: subarachnoid, intraventricular, intraparenchymal, and epidural. To improve the generalization abilities and lessen the overfitting tendencies of ResNet and DenseNet121, two sophisticated convolutional neural network architectures, we used regularization techniques like dropout, label smoothing, and data augmentation. The results of the experiment showed that both models performed well across all classifications and correctly differentiated between different types of bleeding. The findings imply that decision-making in radiology workflows can be aided by deep learning algorithms. By detecting cerebral hemorrhage more rapidly and precisely, this could result in a quicker diagnosis, less disagreement among observers, and better emergency care. Keywords: Intracranial Hemorrhage, Deep Learning, CT Imaging, ResNet, DenseNet121, Medical Image Classification.