Deep Learning-Based Multi-Class Stroke Detection Using CNN

Stroke is a serious condition caused by interrupted blood flow to the brain, leading to severe damage or death if not diagnosed promptly. Traditional methods like manual MRI and CT scan analysis are time-consuming and prone to human error. Earlier models were limited to binary classification, only detecting stroke presence. To address this, we developed a CNN-based model that not only detects stroke but also classifies it into ischemic, hemorrhagic, or normal cases. Our multi-class approach offers faster, more accurate, and detailed diagnosis, reducing human error and improving patient outcomes.