###### Abstract
The slice-wise pre-trained CNN (ResNet-50 and SE-ResNeXt-5 architectures) extracted features from every image, while the LSTM linked them across slices. [PERSON] et al. [9] proposed a CNN architecture, employing two fully  
Figure 1: Sample non-contrast computed tomography (CT) slices with various intracranial hemorrhage (ICH) subtypes in the top row (**a–f**). Yellow arrows indicate the areas of hemorrhage. Complex cases are shown in the bottom row: combinations of multiple ICH subtypes simultaneously (**g–k**) or barely visible ICH symptoms (**l**). connected layers and 37,084 training images to achieve ICH presence detection. The achieved accuracy was equal to 95%. [PERSON] et al. [19] designed a ResNexT CNN model for classification between five ICH subtypes, using the Adam optimizer and a dataset of 674,258 CT slices. The accuracies were equal to 82.8%, 81.8%, 82.0%, 89.3%, and 83.5% for EDH, SDH, SAH, IVH, and IPH, respectively. [PERSON] et al. [12] employed a joint 3D CNN and recurrent neural network (RNN) to detect ICH and recognize its five subtypes. The accuracy of detecting any bleeding exceeded 98%, yet in individual subtypes it varied between 75% and 96%. [PERSON] et al. [20] proposed a 3D CNN for various IPH subtype classification: healthy brain, SAH, IPH, acute subdural hemorrhage (ASDH), and brain polytrauma hemorrhage (BPH). The dataset included 399 volumetric CT scans (approx. 12,000 images). Two types of classification were performed: two-class (normal vs. a specific ICH) and four-class (normal vs. all considered ICH subtypes). The authors employed the F1 score to assess their approaches and obtained 70.6-95.2% for the two-class cases and 68.4% for the multiclass analysis. [PERSON] et al. [18] proposed a combination of a CNN, autoencoder network, and a heat map method. The dataset was processed using an autoencoder network with heatmaps generated based on every image. The outcome subjected to the augmentation process constituted the input to the pre-trained CNN (AlexNet architecture). The classification employed a support vector machine (SVM). The authors reported 98.6% accuracy, 98.1% sensitivity, and 99.0% specificity for detecting any hemorrhage using a dataset consisting of 2101 images with augmentation. [PERSON] et al. [21] compared the results of the AlexNet CNN-SVM combination and two conventional CNN architectures (CNN and pre-trained AlexNet model). The first one produced evaluation metrics better than traditional architectures: 93% accuracy, 95% sensitivity, and 90% specificity for the ICH presence detection over a database containing 12,635 images. [PERSON] et al. [22] proposed an integration of two neural network architectures to perform ICH subtype classification: ResNeXt-101 and bidirectional long short-term memory (BiLSTM). They compared model performance with evaluation metrics calculated based on annotations collected from three radiologists. The obtained accuracy exceeded 96% in ICH subtypes with a weighted mean log loss equal to 0.04989 in the test dataset. However, reduced sensitivity over highly imbalanced data suggests that the F1 score would reflect the system's performance more realistically. In this study, we propose a method for detecting various subtypes of intracranial hemorrhage (SDH, EDH, IPH, IVH, and SAH) in the brain CT scans based on a double-branch CNN for feature extraction and two different classifiers. According to the literature reports, binary detectors of individual subtypes frequently feature higher precision than multiclass approaches [8; 10; 12; 15; 23]. Moreover, a definitive decision from the multiclass framework is often partially wrong since a single slice can present more than one subtype of the ICH. Thus, we use individually trained instances of the proposed architecture to detect each ICH subtype separately. Before feature extraction and classification, preprocessing is applied, including a skull removal algorithm. Three intensity windows are employed to transform the original CT slice.