## 5 Results and Discussion
### Voxel-Based Classification Results
#### 5.3.2 Lung Cancer Staging  
Similar results are realized with the staging scenario as shown in Tables 8-10. However, the performance measures of SS + ALT-DENSE and RPS+ResNet-50 indicate lower performance compared to the phenotyping scenario. Such a finding may be attributed to the fact that the data is stratified into four classes instead of three. On average, compared to RPS + ResNet-50 and RPS+ Inception V3 on the three datasets, \(DETECT\)-\(LC\) records higher performance. The differences reached 0.29, 0.25, 0.37 and 0.35 in terms of Acc, Sn, F1-score and AUC, respectively against RPS + ResNet-50. Also, it achieves higher performance compared to RPS+ Inception V3 with 0.14, 0.38, 0.33 and 0.35 on the Radiomics-Genomics dataset. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Acc** & **Sn** & **F1-Score** & **AUC** \\ \hline [PERSON] et al. [15] & 0.77 & 0.56 & NA & 0.71 \\ [PERSON] et al. [16] & 0.74 & 0.81 & 0.76 & 0.78 \\ [PERSON] et al. [17] & 0.87 & 0.70 & 0.71 & 0.75 \\ [PERSON] et al. [18] & 0.74 & 0.77 & NA & 0.78 \\ \(DETECT\)-\(LC_{avg}\) & 0.94 & 0.89 & 0.92 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: State of the Art Tumor Phenotyping Results on TCIA NSCLC Datasets. Figure 12: Summary of Phenotyping Results presenting confusion matrices and learning curves on three datasets (**a**) Radiomics (**b**) Radiomics Genomics (**c**) Radio Genomics. The learning curves present mean square error (mse) for training and testing and Accuracy for training and testing splits. Other performance aspects that are noted are the model building and training time together with the GPU usage. Figure 13 shows the enhanced performance of ALT-CNN-DENSE _Net_ given both performance aspects. The improvement can be attributed to the reduced architecture depth resulting from the successive early pooling layers combined with the residuals connections. Table 11 sketches the performance of the related studies applied on TCIA NSCLC Datasets and \(DETECT\)-\(LC\) average staging results. [PERSON] et al. [22] performed TNM staging on NSCLC Radiogenomics dataset. [PERSON] et al. [20] used NSCLC Radiogenomics and NSCLC Radiomics-Genomics datasets as training and validation sets for binary staging, whereas [PERSON] et al. [21] experimented with the three datasets for seven classes T-staging only. The results of [PERSON] et al. [21] were provided as collective averages. Although \(DETECT\)-\(LC\) results cannot be directly compared with those of [PERSON] et al. [20] and [PERSON] et al. [21] due to the difference in staging approach, their results are reported for completeness and to give a clear estimate of \(DETECT\)-\(LC\) performance. For instance, despite the simplified binary staging approach of [PERSON] et al. [20], \(DETECT\)-\(LC\) managed to attain higher performance metrics. Compared to [PERSON] et al. [22] on NSCLC Radiogenomics \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc** & **Sn** & **F1-Score** & **AUC** \\ \hline RPS + ResNet-50 & 0.72 & 0.67 & 0.62 & 0.51 \\ RPS + InceptionV3 & 0.83 & 0.57 & 0.61 & 0.55 \\ \(DETECT\)-\(LC\) & 0.93 & 0.88 & 0.92 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Lung Cancer Staging Performance Evaluation on NSCLC Radio Genomics. Figure 13: Comparison between ALT-CNN-Dense Net, RESNET-50 and InceptionV3 in terms of Computation time and GPU usage. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Acc** & **Sn** & **F1-Score** & **AUC** \\ \hline SS + ALT-DENSE _Net_ & 0.51 & 0.54 & 0.53 & 0.41 \\ RPS + ResNet-50 & 0.56 & 0.59 & 0.57 & 0.47 \\ RPS + InceptionV3 & 0.8 & 0.6 & 0.77 & 0.52 \\ \(DETECT\)-\(LC\) & 0.97 & 0.89 & 0.96 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Lung Cancer Staging Performance Evaluation on NSCLC Radiomics (Lung 1).