## 5 Conclusions  
An accurate lung tumor classification is a crucial role for early diagnosis. Computer aided design can considerably reduce clinicians' workload. However, obtaining open access medical images is difficult. Therefore, the GAN was used to augment the data set to alleviate the data shortage problem. With data augmentation, the overall accuracy of the CNN improved by 2.73%. Moreover, tuning the parameters in CNN has become another issue to face nowadays. In this study, the Taguchi method was implemented for selecting optimal parameters through fewer experiments. The experimental results revealed that the accuracy of using the optimal parameter combination can reach 99.86%. The present study only discussed the lung tumor classification and the optimizer for CNNs only took three parameters in the first and fifth layers as consideration. Further research will entail clinical application and optimizer improvement, such as adjusting the parameters of each layer to obtain the best parameter combination or implementing the optimizer in different network architectures. The method can also be applied to other medical applications, such as breast, brain, and liver cancer classification.  
Conceptualization, C.-H.L. and C.-J.L.; Methodology, C.-J.L.; Software, C.-H.L. and Y.-C.L.; Data Curation, C.-J.L. and S.-H.W.; Writing-Original Draft Preparation, C.-H.L. and Y.-C.L.; Writing-Review & Editing, C.-J.L.; Supervision, C.-J.L. and S.-H.W. Funding Acquisition, C.-J.L. and S.-H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of the Republic of China, grant number MOST 109-2634-F-009-031.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Size of Data Set** & **Factor** & & **C1** & & & **C5** & \\  & **KS** & **S** & **P** & **KS** & **S** & **P** \\ \hline Original & 9 & 4 & 2 & 3 & 1 & 1 \\ Double & 9 & 4 & 2 & 3 & 3 & 1 \\ Treble & 13 & 4 & 2 & 3 & 1 & 1 \\ Quadruple & 11 & 2 & 2 & 3 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Best factors of each quantity of images in experiment 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & \multicolumn{4}{c}{**AlexNet**} & \multicolumn{4}{c}{**Best Parameter Combination**} \\ \hline
**Size of Data Set** & **Original** & **Double** & **Triple** & **Quadruple** & **Original** & **Double** & **Triple** & **Quadruple** \\ \hline First (\%) & 95.67 & 98.09 & 98.46 & 99.05 & 99.10 & 99.36 & 99.76 & 99.89 \\ Second (\%) & 98.07 & 99.51 & 99.5 & 99.35 & 99.69 & 99.35 & 99.68 & 99.93 \\ Third (\%) & 97.30 & 98.80 & 99.3 & 98.10 & 99.75 & 99.9 & 99.73 & 99.94 \\ Fourth (\%) & 97.84 & 97.48 & 97.35 & 99.88 & 99.53 & 99.87 & 99.86 & 99.80 \\ Fifth (\%) & 96.63 & 99.14 & 98.76 & 99.44 & 98.56 & 99.87 & 99.84 & 99.76 \\ Average (\%) & 97.10 & 98.60 & 98.67 & 99.16 & 99.326 & 99.67 & 99.774 & 99.86 \\ SD & 0.974 & 0.818 & 0.849 & 0.665 & 0.498 & 0.288 & 0.075 & 0.080 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Comparison of AlexNet with Taguchi method with original AlexNet.  
**Acknowledgments:** The authors would like to thank the support of the Intelligent Manufacturing Research Center (iMRC) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.  
**Conflicts of Interest:** The authors declare no conflict of interest.