Multilayer self-attention fusion pyramid network for enhanced image classification

In recent years, deep learning has made remarkable strides, significantly propelling the field forward. However, traditional CNNs still face limitations in handling multi-scale information and capturing global features. To address these issues, we propose the Multi-Layer Self-Attention Fusion Pyramid Network (MSAPN). MSAPN combines ConvNeXt, feature pyramids, and self-attention modules to enhance the capability to integrate multiscale features and model global dependencies. Our network demonstrates superior performance across multiple public datasets, showcasing significant improvements in classification accuracy.