Deep Learning-Based Classification of Ischemic Stroke Using Brain CT Scans

DOI

10.2991/978-94-6239-664-7_43How to use a DOI?

Keywords

Ischemic stroke; CT scans; Deep Learning; Convolutional Neural Networks (CNNs); DenseNet121; ResNet18; Medical image analysis; Transfer learning; Image classification; Automated diagnosis; Clinical decision support

Abstract

The timely clinical intervention of ischemic stroke in brain CT scans requires the early and accurate identification of its presence in the brain but this is not easy as the imaging characteristics are subtle. This paper demonstrates a well-validated deep learning model with architecture-based DenseNet121 and ResNet18 to identify an ischemic stroke on a large-scale CT dataset of 6,653 scans extensively enhanced to approximately 20,000 images to deal with the issue of class imbalance. In addition to using popular CNN models, our work innovates the field with the use of strict k-fold cross-validation and home-based test validation, which guarantees the strength of the results as well as their successful generalization. Both models are highly accurate (DenseNet121: 98.20%, ResNet18: 97.97%) and compete well with the current state-of-the-art methods. The findings indicate relevant clinical applicability, which provides the possibility to provide quick and dependable automated stroke diagnostics in the emergency department, which may benefit clinicians and decrease diagnostic time and enhance patient outcomes. This paper provides a standard of a proven CNN-based stroke classification system and indicates the future research to improve clinical integration.

Copyright

© 2026 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  - MD Abdullah Ibne Aziz
AU  - Faisal Imran
AU  - Ahmed Rahin Raihan
AU  - Sadia Jaman
AU  - Tasnimul Intazam Asif
AU  - Syed Khairul Hasan
AU  - Gazi Faizul Islam
PY  - 2026
DA  - 2026/06/08
TI  - Deep Learning-Based Classification of Ischemic Stroke Using Brain CT Scans
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 623
EP  - 635
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_43
DO  - 10.2991/978-94-6239-664-7_43
ID  - Aziz2026
ER  -