Summary In recent decades, intracranial hemorrhage detection from computed tomography (CT) scans has gained considerable attention among researchers in the medical community. The major problem in dealing with the Radiological Society of North America (RSNA) dataset is a three dimensional representation of CT scan, where the labeled data are scarce and hard to obtain. To highlight this problem, a novel learned fully connected separable convolutional network is proposed in this research article. After collecting the CT scans, data augmentation is used to generate multiple image variations to improve the capacity of the proposed model generalization. Based on the albumentations library, the transformations are selected for data augmentation such as brightness adjustment, horizontal flipping, shifting, rotation, and scaling. The intracranial hemorrhage subtype classification is accomplished utilizing a learned fully connected separable convolutional network which significantly classifies six classes as any, intraparenchymal, subarachnoid, epidural, intraventricular, and subdural. In the resulting phase, the learned fully connected separable convolutional network obtained an average accuracy of 98.63%, sensitivity of 73.32%, specificity of 99.49%, and area under the curve of 98.98%, where the obtained results are effective compared with ResNet‐50, SE‐ResNeXt‐50, ResNeXt‐101, and ResNeXt‐101 with bidirectional long short term memory network.