###### Abstract  
Brain hemorrhage is a severe threat to human life, and its timely and correct diagnosis and treatment are of great importance. Multiple types of brain hemorrhage are distinguished depending on the location and character of bleeding. The main division covers five subtypes: subdural, epidural, intraventricular, intraparenchymal, and subarachnoid hemorrhage. This paper presents an approach to detect these intracranial hemorrhage types in computed tomography images of the head. The model trained for each hemorrhage subtype is based on a double-branch convolutional neural network of ResNet-50 architecture. It extracts features from two chromatic representations of the input data: a concatenation of the image normalized in different intensity windows and a stack of three consecutive slices creating a 3D spatial context. The joint feature vector is passed to the classifier to produce the final decision. We tested two tools: the support vector machine and the random forest. The experiments involved 372,556 images from 11,454 CT series of 9997 patients, with each image annotated with labels related to the hemorrhage subtypes. We validated deep networks from both branches of our framework and the model with either of two classifiers under consideration. The obtained results justify the use of a combination of double-source features with the random forest classifier. The system outperforms state-of-the-art methods in terms of F1 score. The highest detection accuracy was obtained in intraventricular (96.7%) and intraparenchymal hemorrhages (93.3%). intracranial hemorrhage; computer-aided diagnosis; computed tomography; deep learning; random forest +
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Depending on the brain's anatomic site of bleeding, several ICH subtypes can be distinguished (Figure 1). Subdural hemorrhage (SDH) refers to bleeding between the dura and the arachnoid, whereas the epidural subtype (EDH) involves bleeding between the dura and the bone. Both frequently result from traumatic injuries. Intraparenchymal hemorrhage (IPH) is bleeding within the area of brain parenchyma. A hemorrhage inside the ventricular system is known as intraventricular (IVH). Finally, blood within the subarachnoid space indicates subarachnoid hemorrhage (SAH) [1,5]. One of the leading causes of SAH is a ruptured cerebral aneurysm [3,4]. Hemorrh detection and classification are challenging due to similarities between the various ICH subtypes (e.g., SDH vs. EDH) and subtle differences between healthy and bleeding tissues. These are barely noticeable to the inexperienced observer. Figure 1 presents examples of selected CT slices featuring the physiological brain appearance and ICH subtypes under consideration. In recent years, we have observed increasing interest among researchers in deep learning methods for image classification and segmentation. Convolutional neural networks (CNNs) have gained popularity due to their reliability and efficiency, becoming significant factors in medical diagnosis support [6,7]. In general, CT scans are 3D structures composed of a stack of 2D slices. Thus, operating on image voxels is possible, but may require large computational complexity. A technique to avoid the latter is to either process slices individually or employ the 3D context in a less complex way. Several deep learning approaches to intracranial hemorrhage detection and classification have been proposed, most of them in the last three or four years. Different studies address either one-class detection related to a single class of ICH present in a CT scan [8, 9, 10], multi-class classification distinguishing ICH subtypes [11, 12, 13, 14, 15], or ICH pixel area detection within individual images [7, 16, 17]. The popularity of pre-trained CNN models can be observed, for example for VGG [8], MobileNet [15], AlexNet [18], and ResNet-18 [14]. [PERSON] et al. [11] reported the results of a combination of a convolutional neural network and a long short-term memory (LSTM) for hemorrhage classification.