StrokeNeXt用双分支结构提升脑卒中CT影像分类准确率至98.8%。
StrokeNeXt: A Siamese-encoder Approach for Brain Stroke Classification in Computed Tomography Imagery
- 采用双卷积神经网络编码器+轻量解码器融合特征
- 在6774张CT图像上达98.8%准确率与F1分数
- 速度快、误判率低，适合临床快速诊断
我们提出StrokeNeXt，一种用于二维计算机断层扫描（CT）图像中脑卒中分类的模型。该模型采用双分支设计，包含两个ConvNeXt编码器，其特征通过基于堆叠一维操作的轻量级卷积解码器进行融合，包括瓶颈投影与变换层，以及紧凑的分类头。模型在包含6,774张CT图像的精选数据集上进行评估，同时解决脑卒中检测与缺血性/出血性亚型分类问题。StrokeNeXt持续优于卷积和Transformer基线模型，在准确率与F1分数上达到最高0.988。配对统计检验确认性能提升具有显著性，各类别敏感性和特异性表现稳健。校准分析显示预测误差低于对比方法，混淆矩阵结果表明误分类率极低。此外，模型具备低推理时间与快速收敛特性。
原文摘要 · Abstract (English)
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.
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