Timely and accurate diagnosis of gastric cancer is critical for effective treatment planning and improved patient outcomes. Histopathological examination remains the gold standard for diagnosis; however, existing image classification models often struggle to simultaneously capture fine-grained local tissue patterns and long-range contextual information, which may limit their diagnostic accuracy. To address this issue, we propose a hybrid image classification framework: MobileNetV2-Inception-Swin Transformer based Hybrid Network, termed MV2I SwinNet in short, where MV2I denotes the integration of MobileNetV2-based modules and an Inception-inspired multi-scale design. lightweight convolutional feature extraction with a Swin Transformer to jointly model local structural details and global contextual dependencies in gastric histopathological images. Specifically, redesigned MV2 Inception blocks are employed to enhance multi-scale feature representation with limited computational overhead, while a hierarchical Swin Transformer is used to capture global contextual information. Experimental results on the public GasHisSDB dataset demonstrate that MV2I SwinNet consistently outperforms classical convolutional neural networks, Transformer-based models, and existing hybrid approaches. These results indicate that the proposed method provides an effective and reliable solution for computer-aided gastric cancer diagnosis.
Lin et al. (Fri,) studied this question.