Assistive Brain Hemorrhage Detection for Early Diagnosis

This project aims to address the critical need for rapid and accurate diagnosis of intracranial hemorrhages (ICH), which are life-threatening medical emergencies. By leveraging Deep Learning, specifically Convolutional Neural Networks (CNN)), the system automates the detection and classification of hemorrhage types (Epidural, Intraparenchymal, Intraventricular, Subarachnoid, and Subdural) from CT scan slices. The solution is designed to assist radiologists by providing a prioritized "second opinion," reducing human error and diagnostic delays in high-pressure clinical environments.

Poster