# ResGDANet: An Efficient Residual Group Attention Neural Network for Medical Image Classification
## 4 Experiments
### Dataset and Image Preprocessing  
COVID19-CT [36]: This dataset comprises medical images and COVID-19-related publications collected by [PERSON] et al. [37] from medRxiv2 and bioRxiv3. It includes 349 CT  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Loss Function & Cross entropy \\ Optimizer & Adam \\ Lr\_scheduler & CosineAnnealingLR (T\_max = 32) \\ Batch\_size & 16 \\ Epochs & 120 \\ Learning\_rate & \(3.5\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental hyper-parameters.  
scan images confirmed as COVID-19 positive and 397 CT scan images classified as normal or negative for other diseases. The dimensions of the images vary between 143 \(\times\) 76 and 1637 \(\times\) 1225. Following the original data division strategy, we divided the dataset into three parts--training set, validation set, and test set--in a ratio of 0.6:0.15:0.25.  
ISIC2018 [38]: In the evaluation of the ResGDANet model, we used the ISIC2018 skin lesion diagnosis dataset. This dataset consists of seven categories, totaling 10,015 images. It includes 6705 melanocytic nevus images, 115 dermatofibroma images, 1113 melanoma images, 327 actinic keratosis images, 1099 benign keratosis images, 514 basal cell carcinoma images, and 142 vascular lesion images. All images in the dataset have a size of 650 \(\times\) 450 pixels. We divided the dataset into training, validation, and testing sets in a ratio of 0.6:0.15:0.25 using stratified sampling.  
Examples of ISIC2018 and COVID19-CT images are shown in Figure 7.  
For image preprocessing, we applied normalization and data augmentation techniques to enhance the model's generalization ability and avoid overfitting. All preprocessing operations were performed after the dataset was divided to ensure that no information would leak between the training, validation, and test sets.  
To improve the model's generalization ability, various data augmentation methods were applied to the training set during the data preprocessing phase. These methods included random horizontal flipping and random vertical flipping, each with a probability of 50%; random rotation with an angle range of \(-15^{\circ}\) to \(15^{\circ}\), to simulate rotation variations of images in natural environments; and random adjustments of the image's brightness, contrast, and saturation, with adjustment ranges from 0.8 to 1.2, to improve the model's adaptability to images under different lighting conditions. It is important to emphasize that data augmentation was only applied to the training set, while the validation and test sets retained the original data to ensure the fairness and consistency of performance evaluation.  
Figure 7: Original images in ISIC2018 and COVID19-CT datasets.  
All images were normalized. The normalization used the mean and standard deviation of the ImageNet dataset: mean of [0.485, 0.456, 0.406] and standard deviation of [0.229, 0.224, 0.225]. The specific normalization formula is  
\[X_{\text{normalized}}=\frac{X-\text{mean}}{\text{std}} \tag{19}\]  
where \(X\) is the original pixel value and \(X_{\text{normalized}}\) is the normalized pixel value. In addition, for images smaller than 224 \(\times\) 224, proportional scaling was first applied to adjust the shorter side of the image to 256 pixels, followed by a crop of a 224 \(\times\) 224 region from the center of the scaled image to ensure the consistency and adaptability of the input image.