Brain Stroke Detection Using Deep Learning: BiLSTM Based Approach on CT scans

DOI

10.2991/978-94-6463-858-5_223How to use a DOI?

Keywords

Bidirectional Long Short-Term Memory; Computed Tomography (CT) Imaging; VGG19

Abstract

Brain strokes are a significant cause of mortality and disability worldwide, necessitating early detection for effective intervention. Traditional diagnostic methods relying on manual interpretation of CT scans are often time- consuming and prone to errors. To address this, we propose an automated brain stroke detection system using deep learning techniques. Specifically, we utilize the VGG19 model for feature extraction and a Bidirectional Long Short- Term Memory (BiLSTM) network for classification. The proposed approach effectively identifies stroke- affected regions in CT scans, achieving high accuracy and reliability. The incorporation of deep learning significantly enhances detection efficiency, reducing reliance on human interpretation and mitigating potential diagnostic errors. Experimental results demonstrate the superior performance of BiLSTM in stroke classification, ensuring improved diagnostic support for healthcare professionals and facilitating more timely treatment interventions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - T. Lakshmi Praveena
AU  - Pothuraju Sriharshitha
AU  - Budda raju
AU  - Varshitha Dharmavaram
AU  - Afrah  Faaseya
AU  - Kunchavarapu  Susmitha
PY  - 2025
DA  - 2025/11/04
TI  - Brain Stroke Detection Using Deep Learning: BiLSTM Based Approach on CT scans
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2680
EP  - 2687
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_223
DO  - 10.2991/978-94-6463-858-5_223
ID  - Praveena2025
ER  -