# ResGDANet: An Efficient Residual Group Attention Neural Network for Medical Image Classification
## 1 Introduction
restricted receptive fields and a lack of cross-channel and cross-spatial interaction functionalities, which could hinder its effectiveness in direct medical imaging scenarios. Therefore, manual adjustments to ResNet are usually required for specific tasks. Although such manual adjustments can improve performance to some extent, they also limit generalizability and efficiency in broader medical applications. For instance, Res2 Net [10] enhanced ResNet through improved multi-scale feature representation, ResNeXt [11] achieved higher accuracy using grouped convolutions, and CBAM [12] strengthened feature representation through the introduction of spatial attention modules. Beyond ResNet and its variants, the emergence of ViT (Vison in Transformers) has opened new avenues for medical image processing, and more research has begun to utilize ViT and its derivatives in this domain. MC-ViT [13] uses two sub-networks to predict slice-level pathological information and whole-slide-level thymoma subtypes, significantly improving the accuracy of thymoma subtype classification. The MaxCerVixT [14] model combines the multi-axis vision transformer (MaxViT) architecture, ConvNeXtv2 blocks, and GRN-based MLP layers, effectively improving the accuracy and inference speed of cervical cancer detection. ResGANet [15], introduced in 2022, enhances feature representation through grouped attention mechanisms, yet still exhibits limitations in addressing rotation invariance and local feature capture. Therefore, although existing methods have improved in accuracy, they are still insufficient in addressing key challenges in medical image analysis, such as rotation invariance, local feature capture, and cross-channel interaction. To overcome these limitations, we propose the Residual Group Dual-Channel Attention Network (ResGDANet), which substantially enhances model performance in medical classification tasks by incorporating Dual-Channel Attention Fusion (DCAF) and RMT modules [16], showing stronger adaptability and robustness, especially in terms of rotation invariance, local feature capture, and cross-channel interaction. This paper introduces ResGDANet, an innovative medical image classification method built upon ResGANet, and our proposed attention module designed for enhanced feature representation. The primary contributions of this work are:  
1. Dual-Channel Attention Fusion (DCAF) module: Employs a dual-branch architecture combining global average pooling and max pooling to enhance the network's capacity for capturing locally significant features, while integrating channel attention and coordinate attention to improve the robustness and accuracy of feature representations. 2. RMT module: By leveraging the retention mechanism of Retentive Networks, the RMT module allows the model to efficiently capture and preserve critical information in the data when facing rotating objects. Simultaneously, it incorporates the global context modeling ability of Vision Transformers to comprehend the connections between different components, leading to more effective feature extraction. Through the Manhattan Self-Attention (MaSA) mechanism, the model performs better in handling rotation invariance issues, significantly improving the performance of medical image classification tasks. The first chapter emphasizes the research background and significance of image classification while providing an overview of recent developments in convolutional neural networks within the field of medical image analysis. The second chapter highlights current work pertaining to medical image classification tasks, encompassing residual networks, their various derivatives, and attention mechanisms. The third chapter presents ResGDANet, designed for image classification based on ResGANet, which begins by introducing the network model's components, then elaborates on the model's design and implementation methodology, including ResGANet, the Dual-Channel Attention Fusion (DCAF) module, and the RMT module.