# ResGDANet: An Efficient Residual Group Attention Neural Network for Medical Image Classification
## 1 Introduction  
As social productivity rapidly develops and quality of life continues to improve [1], there is a growing demand for medical imaging diagnostics among patients. Medical imaging (including X-rays, CT scans, pathological images, and MRI) serves as crucial reference material for imaging physicians' diagnostic work, as shown in Figure 1. Traditionally, the task of interpreting medical images has primarily fallen to radiologists and clinicians. However, current deep learning methods still face numerous challenges in medical image classification, such as insufficient model generalization, sensitivity to noisy data, and high computational resource consumption. Addressing these issues is crucial for improving the accuracy and efficiency of medical image classification. Machine learning algorithms can rapidly detect anomalies through image feature analysis, effectively complementing manual diagnostic limitations and alleviating physicians' workload. Nevertheless, medical imaging data often suffer from restricted dimensions and poor signal-to-noise ratios, which poses challenges for deep learning models in preciselyidentifying abnormalities using only visual information [2]. Thus, the task of accurately and efficiently categorizing medical images to support physicians in diagnosing diseases continues to be a significant challenge within medical image analysis. Consequently, this research seeks to improve medical image classification performance by refining the architecture of the model and attention modules, targeting higher classification accuracy and greater robustness to noisy data. Although deep learning technology has shown great potential in medical image analysis, its application in clinical practice has raised a series of ethical issues, particularly regarding data privacy. Medical image analysis faces the challenge of small sample sizes, and the traditional solution is to aggregate data from multiple sites. However, due to strict privacy protection policies, such as HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation), directly sharing medical image data is not feasible. Federated learning, as a solution for collaborative training without sharing data, can effectively protect data privacy and demonstrates its effectiveness and potential in addressing data privacy issues in medical image analysis [3; 4]. The rapid advancement of deep learning in recent years has led to remarkable progress in multiple areas, including image classification, object detection, and semantic segmentation. Computer-assisted diagnostic technology employs deep learning methods to analyze medical images and patient data [5], enabling both condition assessment and clinical decision support. The AlexNet model's excellent performance on ImageNet dataset classification tasks marked a breakthrough in deep learning for image classification [6]. Subsequently, VGG-Net improved classification accuracy by increasing network depth and reducing parameter count [7]. GoogLeNet reduced parameter count through the Inception module and optimized computational resource usage [8]. Nonetheless, these methods face limitations in medical image classification, including the oversight of multi-scale feature extraction and a lack of model interpretability. When adapted to medical image classification tasks, their performance typically lags significantly behind their initial target tasks. Currently, mainstream methods in medical image analysis are still based on ResNet and its variants. Given its modular design and robust feature extraction capabilities, ResNet demonstrates remarkable adaptability to diverse tasks in medical image processing [9]. Nonetheless, ResNet was primarily conceived for image classification, characterized by  
Figure 1: Different types of skin diseases in ISIC2018. (**a**) Actinic keratosis; (**b**) basal cell carcinoma; (**c**) benign keratosis; (**d**) dermatofibroma; (**e**) melanoma; (**f**) melanocytic nevus; (**g**) vascular lesion.