Leo Thomas Ramos

D convolutional fusion path with transformation layers, and a classification head. • We present StrokeNeXt, an approach for brain stroke classification that integrates dual-branch feature extraction and a lightweight decoder. • We demonstrate that StrokeNeXt achieves high accuracy in distinguishing between stroke and normal cases using real-world CT scan data. • We show that the model also performs effectively in differentiating among various stroke subtypes. • We show that StrokeNeXt performs notably when compared to other state-of-the-art models from the literature. • We establish a baseline architecture and performance benchmark that can serve as reference for future research. 2 Related work Stroke identification has been widely explored using diverse paradigms. Early approaches relied on traditional classifiers such as Random Forest [4] and ElasticNet [8], which achieved moderate success in stroke detection but were limited by their dependence on handcrafted features and lack of spatial context. Subsequent efforts shifted toward DL, where CNN-based models such as MobileNetV2 [29] improved automation and spatial feature extraction but still exhibited limited representational capacity and robustness. Other architectures have been proposed to enhance feature representation. Models such as P-CNN [11], D-UNet [29], and OzNet [20] improve lesion localization through multi-scale encoding or task-specific optimizations. In parallel, D 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