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With the development of technology, artificial intelligence-based applications are used for support in many areas. The health sector is one of the areas where such applications are widely used. The increase in knowledge in the health sector due to technological development has led to the need for expertise in radiological evaluation. Considering the intensive working hours, the inaccessibility of specialists from every branch in health institutions and the importance of early diagnosis, especially in emergency pathologies, the importance of the need for applications that will support physicians in the diagnosis process is understood. In the scope of the study, Visual Geometry Group (VGG), Residual Neural Network (ResNet) and EfficientNet architectures, which are among the current deep learning methods, were applied to PhysioNet, a recent dataset, in order to detect brain hemorrhages using Computed Tomography (CT) images. The models were compared among themselves and with existing studies in the literature using accuracy, precision, recall and F1 score metrics. With this study, the importance of choosing the appropriate model for the dataset has been demonstrated through current models. The success of the EfficientNet-B2 model was higher than both the studies in the literature and the models evaluated within the scope of the article. The results show that current deep-learning models have the potential to help in the diagnosis of an intracranial hemorrhage. The study is essential in terms of early diagnosis of intracranial hemorrhage by at least alerting general practitioners, who bear the burden of emergency services, to the presence of intracranial hemorrhage and ensuring that the bleeding condition is not overlooked. [ABSTRACT FROM AUTHOR]