StrokeNeXt: A Siamese-encoder Approach for Brain Stroke Classification in Computed Tomography Imagery
arXiv:2602.15087v1 Announce Type: cross Abstract: We present StrokeNeXt, a model for stroke classification in 2D Computed Tomography (CT) images. StrokeNeXt employs a dual-branch design with two ConvNeXt encoders, whose features are fused through a lightweight convolutional decoder based on stacked 1D operations, including a bottleneck projection and transformation layers, and a compact classification head. The model is evaluated on a curated dataset of 6,774 CT images, addressing both stroke detection and subtype classification between ischemic and hemorrhage cases. StrokeNeXt consistently outperforms convolutional and Transformer-based baselines, reaching accuracies and F1-scores of up to 0.988. Paired statistical tests confirm that the performance gains are statistically significant, while class-wise sensitivity and specificity demonstrate robust behavior across diagnostic categories. Calibration analysis shows reduced prediction error compared to competing methods, and confusion m
arXiv:2602.15087v1 Announce Type: cross Abstract: We present StrokeNeXt, a model for stroke classification in 2D Computed Tomography (CT) images. StrokeNeXt employs a dual-branch design with two ConvNeXt encoders, whose features are fused through a lightweight convolutional decoder based on stacked 1D operations, including a bottleneck projection and transformation layers, and a compact classification head. The model is evaluated on a curated dataset of 6,774 CT images, addressing both stroke detection and subtype classification between ischemic and hemorrhage cases. StrokeNeXt consistently outperforms convolutional and Transformer-based baselines, reaching accuracies and F1-scores of up to 0.988. Paired statistical tests confirm that the performance gains are statistically significant, while class-wise sensitivity and specificity demonstrate robust behavior across diagnostic categories. Calibration analysis shows reduced prediction error compared to competing methods, and confusion matrix results indicate low misclassification rates. In addition, the model exhibits low inference time and fast convergence.
Executive Summary
StrokeNeXt, a novel stroke classification model, achieves superior performance in distinguishing between ischemic and hemorrhagic strokes in CT images. Employing a dual-branch design with ConvNeXt encoders and a lightweight decoder, StrokeNeXt outperforms convolutional and Transformer-based baselines, with accuracy and F1-scores reaching 0.988. The model's performance gains are statistically significant, and its calibration analysis indicates reduced prediction error. However, the study's reliance on a curated dataset of 6,774 CT images may limit its generalizability. Nevertheless, StrokeNeXt's robust behavior across diagnostic categories and low inference time make it a promising tool for clinical applications. Further research is needed to explore the model's potential in real-world settings.
Key Points
- ▸ StrokeNeXt employs a dual-branch design with ConvNeXt encoders and a lightweight decoder
- ▸ The model outperforms convolutional and Transformer-based baselines with accuracy and F1-scores reaching 0.988
- ▸ StrokeNeXt's performance gains are statistically significant and its calibration analysis indicates reduced prediction error
Merits
Strength in Design
StrokeNeXt's dual-branch design with ConvNeXt encoders enables the fusion of features from both branches, resulting in improved performance
Superior Performance
StrokeNeXt outperforms convolutional and Transformer-based baselines, demonstrating its effectiveness in stroke classification
Robust Behavior
The model exhibits robust behavior across diagnostic categories, making it a reliable tool for clinical applications
Demerits
Limited Generalizability
StrokeNeXt's performance may not generalize to real-world settings due to its reliance on a curated dataset of 6,774 CT images
Potential Overfitting
The model's performance gains may be due to overfitting to the training dataset, which could lead to reduced performance in real-world applications
Expert Commentary
StrokeNeXt represents a significant advancement in stroke classification, leveraging the power of deep learning to improve diagnostic accuracy. The model's performance is impressive, and its robust behavior across diagnostic categories is a testament to its reliability. However, further research is needed to explore the model's potential in real-world settings and to address concerns regarding its limited generalizability. The implications of StrokeNeXt are far-reaching, with potential applications in clinical decision-making, healthcare policy, and patient outcomes.
Recommendations
- ✓ Further research is needed to explore the model's potential in real-world settings and to address concerns regarding its limited generalizability
- ✓ The development of guidelines for the use of StrokeNeXt in clinical applications would be beneficial to ensure its safe and effective deployment
