StrokeNeXt: A Siamese-encoder Approach for Brain Stroke Classification in Computed Tomography Imagery

TL;DR

StrokeNeXt is a novel dual-encoder model that significantly improves brain stroke classification accuracy in CT images, outperforming existing methods with robust statistical validation and efficient inference.

Contribution

The paper introduces StrokeNeXt, a dual-branch ConvNeXt-based model with a lightweight decoder for accurate and fast stroke detection and classification in CT imagery.

Findings

Achieves up to 98.8% accuracy and F1-score

Outperforms convolutional and Transformer baselines

Demonstrates statistically significant performance improvements

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.