Deep Learning-Based Multi-Class Stroke  Detection  Using CNN

Author(s)	Nikhil Kulkarni, pratik Kuntawar, Niraj Saraf, Gaurav mahore, V.S.Mahalle
Country	India
Abstract	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.
Keywords	— Convolutional Neural Network, Brain Stroke, Ischemic Stroke, Hemorrhagic Stroke, Deep Learning
Field	Medical / Pharmacy
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-04-18
Cite This	Deep Learning-Based Multi-Class Stroke Detection Using CNN - Nikhil Kulkarni, pratik Kuntawar, Niraj Saraf, Gaurav mahore, V.S.Mahalle - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3801
DOI	https://doi.org/10.71097/IJSAT.v16.i2.3801
Short DOI	https://doi.org/g9gdtj

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