Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)

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Genetic Algorithm-Optimized BiLSTM Framework for Enhanced Stroke Diagnosis using Neuroimages

Authors
M. K. Nivodhini1, *, S. Vadivel1, P. Priyadharshini1, M. K. Prem Kumar2, P. Sakthipriya2, V. Sajitha2
1Assistant Professor, Department of Computer Science and Engineering, K S R College of Engineering, Tiruchengode, Namakkal, Tamil Nadu, India
2Student, Department of Computer Science and Engineering, K S R College of Engineering, Tiruchengode, Namakkal, Tamil Nadu, India
*Corresponding author. Email: nivodhiniomk99@gmail.com
Corresponding Author
M. K. Nivodhini
Available Online 23 May 2025.
DOI
10.2991/978-94-6463-718-2_130How to use a DOI?
Keywords
Genetic algorithm; BiLSTM; Stroke prediction; Neurological imaging; User-centred experience; Optimizing hyperparameter settings; Multimodal imaging data; Feature thresholding
Abstract

Challenges in management Stroke is a leading cause of death and disability worldwide that necessitates timely and precise diagnosis for appropriate management. This paper proposes a new Genetic Algorithm-Optimized framework of BiLSTM networks for Stroke diagnosis with Neuroimages. And optimize their selection/parameters through genetic algorithms integrated into a BiLSTM model that captures temporal dependencies in neuroimaging data. Utilizing multimodal datasets (CT and MRI scans) that outperform the traditional approaches in terms of sensitivity and specificity. The framework is tailored to facilitate a range of healthcare settings, with an emphasis on scalability, real-time application, and most importantly, clinical interpretability. Experimental results confirm the robustness of the framework, its computational efficiency, and its potential for large-scale clinical delivery. This could be a milestone for stroke diagnosis and customized treatment.

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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Volume Title
Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series
Advances in Computer Science Research
Publication Date
23 May 2025
ISBN
978-94-6463-718-2
ISSN
2352-538X
DOI
10.2991/978-94-6463-718-2_130How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - M. K. Nivodhini
AU  - S. Vadivel
AU  - P. Priyadharshini
AU  - M. K. Prem Kumar
AU  - P. Sakthipriya
AU  - V. Sajitha
PY  - 2025
DA  - 2025/05/23
TI  - Genetic Algorithm-Optimized BiLSTM Framework for Enhanced Stroke Diagnosis using Neuroimages
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1557
EP  - 1571
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_130
DO  - 10.2991/978-94-6463-718-2_130
ID  - Nivodhini2025
ER  -