Brain Hemorrhage Detection

Brain hemorrhages are life-threatening medical conditions that require immediate and accurate diagnosis to prevent severe neurological damage or death. Timely and accurate diagnosis of brain hemorrhages is crucial for preventing serious medical complications and improving patient outcomes. This study presents an autonomous diagnostic methodology employing neural networks for ICH identification and classification. A deep learning framework utilizing an optimized DenseNet-121 model is implemented to examine brain CT scans and detect different categories of intracranial bleeding. The system aims to address limitations of traditional manual diagnosis, such as time constraints, limited availability of radiologists, and human error. A robust preprocessing pipeline ensures data consistency, while the model integrates attention mechanisms and optimized thresh- olds to improve interpretability and classification accuracy. By streamlining the diagnostic workflow, this AIassisted method supports faster decision-making and more efficient resource utilization in clinical settings. The results highlight the growing role of artificial intelligence in enhancing medical diagnostics and offer a promising solution for improving the speed and reliability of brain injury assessment.