Intracranial hemorrhage (ICH) is a critical subtype of stroke that arises from bleeding within the brain and often leads to severe neurological damage or death if not diagnosed at an early stage. The rapid rise in stroke incidence worldwide, combined with limited access to specialized radiologists, necessitates the development of automated and efficient detection systems that can support early intervention. Leveraging deep learning in medical imaging can significantly reduce diagnostic delays, improve treatment outcomes, and lower stroke-associated mortality. In this study, we propose Hem-CNN, a deep convolutional neural network built upon a pretrained EfficientNetB2 backbone with customized convolutional and pooling layers, designed specifically for stroke-related CT images. Extensive preprocessing techniques, including noise reduction, CLAHE contrast enhancement, and histogram normalization, were applied to ensure image quality. The model achieved an accuracy of 97.80%, precision of 96.86%, recall of 97.37%, specificity of 98.06%, and an F1-score of 97.10%, outperforming models such as VGG19, ResNet152, and EfficientNetB2. The novelty of this study lies in the integration of a lightweight yet highly discriminative CNN architecture with robust preprocessing tailored for medical imaging variability, alongside explainability through Grad-CAM visualization for clinical interpretability. Despite promising performance, the study was limited by dataset size and diversity, highlighting the need for larger multi-institutional validation to ensure broader clinical applicability.