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Deep Learning-Based Multi-Class Stroke Detection Using CNN

Year: 2025

Type: article

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.

Source: International Journal on Science and Technology

DOI: 10.71097/ijsat.v16.i2.3801

Authors: Nikhil Kulkarni -, pratik Kuntawar -, Niraj Saraf -, Gaurav mahore -, V.S.Mahalle -

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4409696131

FWCI: 0.76

Cites:

Cited by: 1

Related to: 10