# Research on Radionuclide Identification Method Based on GASF and Deep Residual Network
## 4 Experimental Results and Analysis
### Improvethe Model  
There is a relationship between the complexity of neural network models and their operating speed. According to the study by [PERSON] et al. (2016), reducing the number of parameters in the network can reduce the computational load, thereby improving speed while maintaining accuracy [19]. Furthermore, smaller convolutional kernels (e.g., \(3\times 3\)) can more precisely capture local features, avoiding the potential information loss that may occur with larger kernels [20]. Removing pooling layers helps preserve more detailed information, thus enhancing the model's training performance [21]. In addition, reducing the number of channels in convolutional layers can lower model complexity and reduce the risk of overfitting [22]. In practical applications, such as medical imaging, single-photon emission computed tomography (SPECT) uses radioac  
Figure 6: Evaluation metrics for nuclide identification results of the original model. tive isotope-labeled drugs to scan lesion areas [23], while positron emission tomography (PET) is used to observe metabolic changes in conditions like heart disease or tumors [24]; in these applications, high accuracy is required, but speed is also crucial. Therefore, optimizing the model structure and improving inference speed is essential. Based on the application requirements, the model was improved. In the ResNet18 model, the number of channels in the convolutional layers from stage 1 to stage 5 was originally 64, 64, 128, 256, and 512, respectively. These were modified to 16, 16, 32, 64, and 128, respectively. Additionally, considering the size of the dataset used in this paper, the \(7\times 7\) convolution and max pooling operations in stage 1 of the ResNet18 network were prone to losing some feature information. Therefore, in the improved model, the \(7\times 7\) convolution was replaced with a \(3\times 3\) convolution, with a stride and padding of 1, and the max pooling layer was removed. This modification helps to preserve as much of the original image feature information as possible. A comparison of the network model structure parameters before and after the improvement is shown in Table 2.  
Using the same dataset, data split ratio, and preprocessing methods, the improved model was trained and tested. The experimental results are shown in Figure 7, where the precision is 99.3%, recall is 99.7%, the F1 score is 99.5%, and the loss value is 0.0031. In the improved model, the recognition results of single-source and double-source nuclides on the test set are presented through confusion matrix plots, as shown in Figure 8. According to the results, the feature peaks of single-source nuclides are relatively clear, and all samples are correctly classified, achieving 100% recognition accuracy. In the case of double-source combinations, when the feature peaks of the nuclides included in the combination are relatively close, the gamma spectra of the two different nuclides may overlap in certain energy ranges, thereby increasing the difficulty of classification for the model. The analysis of the confusion matrix reveals that misclassified samples are usually concentrated among nuclide combinations with similar feature peaks. For example, in the case of \({}^{60}\)Co + \({}^{137}\)Cs and \({}^{60}\)Co + \({}^{226}\)Ra, although the two main gamma peaks of \({}^{60}\)Co are relatively distinct, the main peak of \({}^{137}\)Cs and some low-energy lines of \({}^{226}\)Ra may overlap. A similar situation also occurs in other combinations with close nuclide features, such as \({}^{137}\)Cs + \({}^{232}\)Th and \({}^{226}\)Ra + \({}^{238}\)U. Despite the existence of misclassifications, their proportion is minimal. Through the analysis of the confusion matrix, it is confirmed that nuclide combinations with similar feature peaks are more likely to lead to misclassification, which provides valuable insights for further optimizing the model in the future.