Abstract
Early identification of ischemic stroke is critical for timely intervention and improved patient outcomes. However, detecting early signs of stroke in noncontrast computed tomography (CT) remains challenging due to subtle appearance changes and limited contrast. In this work, we propose a novel approach for early identification of ischemic stroke in noncontrast CT images.
Our method leverages advanced image processing and machine learning techniques to detect subtle signs of ischemia that may be missed by human observers. The framework consists of three key components: (1) a feature extraction module that identifies early ischemic changes, (2) a temporal analysis module that tracks progression over time, and (3) a classification module that provides early stroke detection.
We evaluate our approach on a comprehensive dataset of stroke patients with confirmed diagnoses and follow-up imaging. Experimental results demonstrate that our method significantly improves early stroke detection compared to traditional approaches. The method shows particular effectiveness in detecting subtle signs of ischemia that may precede obvious clinical manifestations.
The proposed framework represents a significant advancement in stroke diagnosis, providing earlier detection that could improve treatment outcomes and patient survival rates.
BibTeX
@article{wu2019early,
title={Early identification of ischemic stroke in noncontrast computed tomography},
author={Wu, Guoqing and Lin, Jixian and Chen, Xi and Li, Zeju and Wang, Yuanyuan and Zhao, Jing and Yu, Jinhua},
journal={Biomedical Signal Processing and Control},
year={2019},
publisher={Elsevier},
doi={10.1016/j.bspc.2019.03.008}
}