Brain hemorrhage is one of the most serious medical diseases, requiring immediate treatment through posttraumatic healthcare. For this life-threatening disease, immediate care involves an urgent diagnosis. Intracranial bleeding is frequently associated with severe headaches and loss of consciousness. When a patient shows these symptoms, expert radiologists examine computed tomography (CT) images of the patient’s brain to locate and diagnose the type of bleeding. On the other hand, the manual examination performed by radiologists is complicated and time-consuming, naturally and unnecessarily delaying the intervention. In this chapter, we examined hemorrhage classification from CT images dataset, with deep learning architectures. In the experimental study, a total of 200 brain CT images were used as test and train. For this aim, different convolutional neural networks such as ResNet-18, EfficientNet-B0, VGG-16, and DarkNet-19 were used to classify brain CT images as normal and as hemorrhage. The accuracy (ACC), sensitivity (SEN), specificity (SPE), and F-score were used as the performance metrics for the classifier performances. The best classification results were ACC 83.50%, SEN 82%, SPE 85%, F-score 83.20%, and MCC 65% with DarkNet-19, respectively.