# HLFSRNN-MIL: A Hybrid Multi-Instance Learning Model for 3D CT Image Classification. _Applied_
## 4 Experiments and Results
### Ablation Experiments on TCIA  
This paper aims to investigate the contribution of the HLFFF and SRNN methods to the classification network. The FLFFF method requires setting the value of \(\alpha\in(0,1)\) to adjust the weight distribution of high frequency and low frequency in the method. Initially, ResNet-50 is utilized as the fundamental feature extraction method of the model. The value of \(\alpha\) is set in the range of 0.1 to 0.9, with an interval of 0.1, yielding a total of nine values. The benchmark model of ResNet-50 combined with the HLFFF method is tested on the TCIA dataset. Table 3 summarizes the performance indicators under different \(\alpha\) values. Figure 5A illustrates the area under the ROC curve under varying \(\alpha\) values. The results indicate that when \(\alpha=0.5\), the model performs optimally, with the AUC and ACC increasing by 7.8% and 6.7%, respectively, compared to the classification model utilizing only ResNet-50.  
The SRNN method needs to set the value of \(w\in R\) to adjust the length of the instance fragment processed by the method. The experiment will be carried out on the basis of the combination of ResNet-50 and HLFFF methods. The value of \(w\) is set between 2 and 9 and the interval is 1, a total of 9 values. The benchmark model combining ResNet-50 and the HLFFF and SRNN methods is also tested on the TCIA dataset. Table 4 shows the performance indicators of the model under different w values. Figure 5B shows the area under the ROC curve under different w values. The results show that the performance of the model is optimal when \(w=5\), which means that each time the sample fragment with  
Figure 5: Regarding the area under the ROC curve under the influence of different \(\alpha\) and \(w\), respectively. (The left picture (**A**) is the ROC curve of the \(\alpha\), and the right picture (**B**) is the ROC curve of the \(w\)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\alpha\) & **AUC** & **ACC** & **SEN** & **SPE** & **F1 Score** & **Precision** \\ \hline \(\alpha=0.1\) & 0.924 & 0.914 & 0.995 & 0.829 & 0.921 & 0.854 \\ \(\alpha=0.2\) & 0.930 & 0.907 & 0.886 & 0.929 & 0.905 & 0.925 \\ \(\alpha=0.3\) & 0.952 & 0.936 & 0.871 & 0.914 & 0.931 & 0.921 \\ \(\alpha=0.4\) & 0.932 & 0.943 & 0.996 & 0.886 & 0.946 & 0.897 \\ \(\alpha=0.5\) & 0.966 & 0.957 & 0.997 & 0.997 & 0.959 & 0.951 \\ \(\alpha=0.6\) & 0.958 & 0.936 & 0.986 & 0.886 & 0.939 & 0.896 \\ \(\alpha=0.7\) & 0.950 & 0.907 & 0.943 & 0.871 & 0.910 & 0.885 \\ \(\alpha=0.8\) & 0.919 & 0.893 & 0.998 & 0.786 & 0.903 & 0.824 \\ \(\alpha=0.9\) & 0.899 & 0.879 & 0.786 & 0.971 & 0.866 & 0.965 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of HLFFF method under different \(\alpha\) values.  
a length of five is input into the SRNN method, the AUC and ACC are increased by 3.3% and 3.5%, respectively, compared with the model using only the HLFFF method.  
In particular, we evaluated the accuracy of the model predictions based on the le-sion site annotations in the original TCIA dataset. By generating weight heat maps for each sample fragment after applying the SRNN method, Figure 6 displays the weight heat maps for four packages. Each weight represents a fragment of length 5. The re-sults demonstrate that the SRNN method adeptly identifies the fragment with the highest lesion weight. The red boxes in the graph denote annotation boxes marked ac-cording to corresponding annotation information, highlighting the lesion sites identi-fied by the SRNN method.