## 5 Results and Discussion
### Voxel-Based Classification Results
#### 5.3.1 Lung Cancer Pathology Phenotyping  
Pathology phenotyping is carried out on the three datasets separately and the results are reported accordingly in Tables 4-6. All the performance metrics in Tables 4-6 portray the superior performance of \(DETECT\)-\(LC\), compared to SS+ALT-DENSE _Net_, RPS + ResNet-50 and RPS+ Inception V3 counterparts. The superior performance highlights the importance of each phase. \(DETECT\)-\(LC\) achieves higher performance when compared to RPS + ResNet-50 on all datasets with a minimum difference of 0.11, 0.15 and 0.21 in terms of accuracy (Acc), sensitivity (Sn) and F1-score respectively on NSCLC Radio Genomics. The minimum AUC difference of 0.25 is on NSCLC Radiomics. A smaller improvement is attained against RPS + Inception V3; nevertheless it managed to score a minimum of 0.06, 0.2 and 0.08 improvement in terms of Acc, F1-score and AUC, respectively. As for the manual static slice selection comparison, an immense performance gap is observed where \(DETECT\)-\(LC\) outperformed SS + ALT-DENSE _Net_ with 0.29, 0.32, 0.31 and 0.42 gap for Acc, Sn, F1-score and AUC respectively. Another parameter worth noting in this experiments is training convergence time, as SS + ALT-DENSE _Net_ recorded seven hours of training time against 48 min for \(DETECT\)-\(LC\). These findings highlight the inability of ALT-DENSE _Net_ to extract distinguishing features from the statically selected slices. This is due to the fact that they contain non-discriminative (closed) lung slices, which dramatically degrades the performance of ALT-CNN-DENSE _Net_. \(DETECT\)-\(LC\) is contrasted to state of the art studies, which experimented on the TCIA NSCLC datasets. [PERSON] et al. (2016), [PERSON] et al. (2015) and [PERSON] et al. (2017) worked on NSCLC Radiomics dataset, whereas [PERSON] et al. (2018) studied the performance of their model on a merged dataset of NSCLC Radiomics, NSCLC Radiogenomics and a private dataset from China Institute. The best results for these related studies are detailed in Table 7. \(DETECT\)-\(LC\) outperforms the state of the art with an overall accuracy improvement ranging from 9 to 22%. Despite that [PERSON] et al. (2017) manifest the best overall accuracy among the reported studies from the literature, it presented a sensitivity (Sn) value of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Acc** & **Sn** & **F1-Score** & **AUC** \\ \hline RPS + ResNet-50 & 0.82 & 0.73 & 0.71 & 0.51 \\ RPS + InceptionV3 & 0.82 & 0.61 & 0.7 & 0.6 \\ \(DETECT\)-\(LC\) & 0.93 & 0.88 & 0.92 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Lung Cancer Phenotyping Performance Evaluation on NSCLC Radio Genomics. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc** & **Sn** & **F1-Score** & **AUC** \\ \hline SS + ALT-DENSE & 0.60 & 0.61 & 0.62 & 0.41 \\ RPS + ResNet-50 & 0.67 & 0.62 & 0.64 & 0.58 \\ RPS + InceptionV3 & 0.89 & 0.7 & 0.81 & 0.71 \\ \(DETECT\)-\(LC\) & 0.96 & 0.93 & 0.93 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Lung Cancer Phenotyping Performance Evaluation on NSCLC Radiomics (Lung 1). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc** & **Sn** & **F1-Score** & **AUC** \\ \hline SPS + ALT-DENSE & 0.60 & 0.61 & 0.62 & 0.41 \\ RPS + ResNet-50 & 0.67 & 0.62 & 0.64 & 0.58 \\ RPS + InceptionV3 & 0.89 & 0.7 & 0.81 & 0.71 \\ \(DETECT\)-\(LC\) & 0.96 & 0.93 & 0.93 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Lung Cancer Phenotyping Performance Evaluation on NSCLC Radiomics (Lung 1). 0.60 for ADC class. This was explained by the limited number of patients in ADC class; however, \(DETECT\)-\(LC\) managed to attain a Sn of 0.83 on the same class. When comparing the average performance of \(DETECT\)-\(LC\) on the three datasets to the state of the art, it is found that \(DETECT\)-\(LC\) surpasses them with a minimum of 0.07, 0.08, 0.16 and 0.09 of Acc, Sn, F1-Score and AUC, respectively.