# Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images
## 3 Experimental Results
### Spie-Aapm  
SPIE-AAPM [20] consists of 22,489 CT images for 68 samples, including 31 benign and 37 malignant samples. The mean responses for the S/N ratios and ANOVA are shown in Tables 8 and 9, respectively. Table 9 shows that four factors--C1_S, C2_P, C1_KS, and C2_F--have a significant impact. From Table 8 and Figure 7, the factors affecting the S/N ratios based on the yield extraction method are \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Accuracy** & **Sensitivity** & **Specificity** & **False Positive Rate (FPR)** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline [PERSON] et al. [22] & 86.02 & 83.91 & 89.32 & 10.68 \\ [PERSON] et al. [2] & 99.51 & 99.71 & 99.71 & 0.29 \\
2D CNN with [PERSON] & 98.83 & 99.97 & 99.93 & 0.06 \\ parametric optimization & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Recognition results of different techniques on the LIDC-IDRI dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Degree of Freedom** & **Sum of Squares** & **Mean Squares** & **F\({}_{\text{factor}}\)** & **\(p\)-Value** \\  & **(DF)** & **(SS)** & **(MS)** & **F\({}_{\text{factor}}\)** & **\(p\)-Value** \\ \hline A (C1\_S) & 1 & 0.34989 & 0.34989 & 4.46 & 0.047 \\ B (C1\_P) & 1 & 0.07722 & 0.07722 & 0.98 & 0.333 \\ C (C2\_S) & 1 & 0.3776 & 0.3776 & 4.81 & 0.040 \\ D (C2\_P) & 1 & 0.17179 & 0.17179 & 2.19 & 0.155 \\ E (C1\_KS) & 2 & 1.60437 & 0.80218 & 10.23 & 0.001 \\ F (C1\_F) & 2 & 0.1853 & 0.09265 & 1.18 & 0.327 \\ G (C2\_KS) & 2 & 0.27429 & 0.13714 & 1.75 & 0.20 \\ H (C2\_P) & 2 & 3.3545 & 1.67725 & 21.38 & 0 \\ Error & 20 & 1.56897 & 0.07845 & & \\ Total & 32 & 8.5957 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ANOVA for the S/N ratios of the accuracy on the LIDC-IDRI dataset. Figure 6: Response graph of S/N ratio values of factor levels on the LIDC-IDRI dataset. affected at each level of the parameters. The most significant factors can be determined by the larger differences in the S/N ratio. The experimental results indicated that factor C2_F has a noticeable delta value of 0.76%, meaning that factor C2_F is the most significant parameter in the model response. In terms of the importance, the parameters were ranked: C2_F > C1_KS > C1_S > C2_P > C1_F > C2_S > C2_KS > C1_P. Hence, the best levels are A1 (level 1 for C1_S), B2 (level 2 for C1_P), C1 (level 1 for C2_S), D2 (level 2 for C2_P), E1 (level 1 for C1_KS), F1 (level 1 for C1_F), G2 (level 2 for C2_KS) and H3 (level 3 for C2_F). Optimum parameter C1_S is 1, C1_P is 1, C2_S is 1, C2_P is 1, C1_KS is 3, C1_F is 4, C2_KS is 5 and C2_F is 32. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{3}{c}{**Degree of Freedom**} & \multicolumn{3}{c}{**Sum of Squares**} & \multicolumn{3}{c}{**Mean Squares**} \\  & **(DF)** & **(SS)** & **(MS)** & **F factor** & **\(p\)-Value** \\ \hline A (C1\_S) & 1 & 1.90796 & 1.90796 & 8.01 & 0.01 \\ B (C1\_P) & 1 & 0.00445 & 0.00445 & 0.02 & 0.893 \\ C (C2\_S) & 1 & 0.37877 & 0.37877 & 1.59 & 0.222 \\ D (C2\_P) & 1 & 1.13952 & 1.13952 & 4.79 & 0.041 \\ E (C1\_KS) & 2 & 1.68899 & 0.84445 & 3.55 & 0.048 \\ F (C1\_F) & 2 & 0.42157 & 0.21078 & 0.89 & 0.428 \\ G (C2\_KS) & 2 & 0.05862 & 0.02931 & 0.12 & 0.885 \\ H (C2\_F) & 2 & 2.53983 & 1.26991 & 5.33 & 0.014 \\ Error & 23 & 4.76109 & 0.23805 & & & \\ Total & 35 & 12.9007 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: ANOVA for the S/N ratios of the accuracy on the SPIE-AAPM dataset.