# Research on Radionuclide Identification Method Based on GASF and Deep Residual Network
## 4 Experimental Results and Analysis
### Verification with Experimental Data  
To evaluate the practical application performance of the proposed nuclide identification model, this study further introduces spectral data obtained from actual measurements for validation. The measured data were provided by a laboratory. Specifically, gamma spectrum data for \({}^{60}\)Co, \({}^{137}\)Cs, and \({}^{226}\)Ra were collected by the laboratory using a gamma spectroscopy acquisition system for each radioactive source, with 600 spectra for each point source. The gamma spectrum data for \({}^{238}\)U and \({}^{232}\)Th were obtained from a sandstone uranium ore logging model established by a radiation exploration metrology station, with 800 spectra for each point source. In total, 3400 spectra were collected for the five radioactive sources. To conduct a more comprehensive validation, three additional radionuclides that were not present in the training set were introduced: \({}^{152}\)Eu, \({}^{133}\)Ba, and \({}^{241}\)Am. These data were also provided by the laboratory, with 600 spectra for each radioactive source, resulting in a total of 1800 spectra for the three new categories. The inclusion of these new categories helps to test the model's generalization ability and assess its performance when faced with new nuclides not seen during training. The 1800 spectra of the three new categories were evenly distributed among the existing categories of \({}^{60}\)Co, \({}^{137}\)Cs, \({}^{226}\)Ra, \({}^{238}\)U, and \({}^{232}\)Th. Since the training set consists of only five categories, the newly introduced nuclides could only be misclassified as one of the existing categories. To test whether the model can correctly identify the new nuclides without misclassifying them as existing categories, this study follows a common approach in machine learning and deep learning: when the number of categories in the test set exceeds that of the training set, the three new radionuclides are defined as an \"unknown\" category. Specifically, for each input gamma spectrum sample, the model outputs the predicted probability for each category. To ensure that new nuclides are not misclassified as known categories, a minimum probability threshold is set. When the maximum predicted probability for a sample is below this threshold, the sample is considered to belong to the \"unknown\" category, and the prediction result is labeled as \"unknown nuclide\" instead of being classified as one of the existing categories [25; 26; 27; 28; 29; 30]. Prior to the official testing, a preliminary test was conducted on 3400 gamma spectra data from five radionuclides: \({}^{60}\)Co, \({}^{137}\)Cs, \({}^{226}\)Ra, \({}^{232}\)Th, and \({}^{238}\)U. The preliminary test was performed three times, each running for 300 epochs, and the overall accuracy of the model in all three rounds of testing was approximately 95.2%, with the loss value approaching 0.05. During this process, the predicted probability corresponding to each correctly classified sample was recorded. The results showed that, for 95% of the correctly classified samples, the predicted probability output by the model was higher than 0.6. Based on this statistical result, the minimum probability threshold was set to 0.6 to ensure that the new nuclides would not be misclassified as existing categories. The mixed data of the \({}^{60}\)Co, \({}^{137}\)Cs, \({}^{226}\)Ra, \({}^{238}\)U, and \({}^{232}\)Th gamma spectra with the three newly introduced radionuclides, \({}^{152}\)Eu, \({}^{133}\)Ba, and \({}^{241}\)Am, were processed using the aforementioned segmented GASP method and input into the trained improved model for testing. After 500 epochs, the test results are shown in Figure 9. Figure 9a displays the accuracy and loss metrics curves, while Figure 9b presents the confusion matrix with the recognition results of the actual measurement data along with the recorded \"unknown\" category.