A DEEP LEARNING MODEL FOR BRAIN STROKE DETECTION USING CT SCANS
Keywords— Brain Stroke Detection, CT Imaging, Deep Learning, CNN, LSTM, Medical Image Analysis, Automated Diagnosis
Abstract—Brain stroke is a serious neurological condition that requires quick and accurate diagnosis to reduce the risk of death and long-term disability. Computed Tomography (CT) scans are commonly used for stroke detection because they are fast and easily available. However, analyzing these images manually can take time and may vary between radiologists. This study presents a deep learning–based system for detecting brain stroke using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. The CNN model helps in extracting important features from CT images, while the LSTM model enhances the classification process. The results show high accuracy and reliability, highlighting the potential of deep learning in supporting medical image analysis and improving clinical decision-making.
: https://doi.org/10.56975/ijvra.v4i3.702264
Registration ID: IJVRA_702264 Published ID: IJVRA2603730
"A DEEP LEARNING MODEL FOR BRAIN STROKE DETECTION USING CT SCANS", IJVRA - International Journal of Versatile Research and Analysis (www.IJVRA.org), ISSN:2984-8903, Vol.4, Issue 3, page no.606-610, March-2026, Available :https://ijpub.org/ijvra/papers/IJVRA2603730.pdf
Paper Reg. ID: IJVRA_702264
Published Paper Id: IJVRA2603730
Research Area: Other area not in list
Country: hyderabad, telangana, India
DOI: https://doi.org/10.56975/ijvra.v4i3.702264
ISSN: 2984-8903 | IMPACT FACTOR: 9.12 Calculated By Google Scholar | ESTD YEAR: 2023
An International UGC CARE JOURNAL PUBLICATION Low Cost (₹599), Scholarly Open Access, Peer-Reviewed, Refereed Journal Impact Factor 9.12 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage, Crossref DOI Member Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJVRA (IJ Publication) Janvi Wave
© 2026 - Authors hold the copyright of this article. This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition.
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). 🛡️ Disclaimer:
The content, data, and findings in this article are based on the authors’ research and have been peer-reviewed for academic purposes only. Readers are advised to verify all information before practical or commercial use. The journal and its editorial board are not liable for any errors, losses, or consequences arising from its use.
About Hard Copy and Transparent Peer Review Report
