Abstract :

Stroke Is One Of The Leading Causes Of Death And Long-term Disability Worldwide, Making Early Diagnosis Essential For Improving Patient Survival And Reducing Permanent Neurological Damage. Recent Advances In Deep Learning Have Provided New Opportunities For Analysing Medical Images With Greater Speed And Accuracy. This Study Presents A Hybrid Deep Learning Model For Automated Stroke Identification Using Neuroimaging, Designed To Detect Stroke From Brain CT Images At An Early Stage. The Proposed Framework Combines Image Preprocessing, Feature Extraction, And A Hybrid Deep Learning Architecture To Accurately Distinguish Stroke Cases From Normal Brain Scans. Relevant Image Features Are Selected To Improve The Learning Process And Enhance Classification Performance. The Developed Model Is Evaluated Using Standard Performance Measures, Including Accuracy, Precision, Recall, And F1-score, To Ensure Reliable Diagnostic Capability. Experimental Results Demonstrate That The Proposed Approach Achieves Superior Performance Compared With Several Conventional Machine Learning Algorithms, Providing More Accurate And Consistent Stroke Identification. The Developed System Can Serve As An Effective Clinical Decision Support Tool By Assisting Healthcare Professionals In Analysing Neuroimages Quickly, Enabling Timely Diagnosis, Appropriate Treatment Planning, And Improved Patient Outcomes.

Published:

30-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1368-1373

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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