## 5 Discussion and Conclusions
For the staging, we achieved an accuracy of 90.6%, a sensitivity of 77.4%, a specificity of 97.4%, a precision of 93.8%, an F1-score of 79.6% and an AUC of 84.6% respectively. Compared with previous works, our CAD was evaluated with more samples and obtained auspicious performances. Unlike most existing CADs for lung cancer diagnosis, our CAD makes possible not only the detection, but also the staging of lung tumor nodules. Our first contribution, the extraction of locational features, has advantages for the staging of tumor nodules, because the severity of lung cancer is strongly associated with the size and location of tumor nodules. Knowing the exact location of tumors reflects accurate staging. Patient survival rates can also be increased by providing precise treatment options depending on the tumor stage. Moreover, locational features produced by our CAD will be useful for the clinical surgery planning of seriously staged tumors. The next contribution, the double stage of classification using BPNN, makes the CDA more robust. As the first stage classification filters only the true tumor nodules, we do not need to extract the locational features from all nodule candidates; this saves both computational effort and time. Nonetheless, our study has some limitations in terms of the need for background knowledge for anatomical lung structures. Moreover, our CAD only focuses on the T-staging of lung cancer. Thus, there are some open issues which can be further upgraded for the N and M-staging of lung cancer. This paper uses four popular public datasets namely LIDC-IDRI, NSCLC-Radiomics-Genomics, NSCLC-Radiomics and NSCLC Radiogenomics. They are available online at [[https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)]([https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI](https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI)), [[https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics](https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics)]([https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics](https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics)), [[https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics](https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics)]([https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics](https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics)), [[https://wiki.cancerimagingarchive.net/display/Public/NSCLC+Radiogenomicsrespectively](https://wiki.cancerimagingarchive.net/display/Public/NSCLC+Radiogenomicsrespectively)]([https://wiki.cancerimagingarchive.net/display/Public/NSCLC+Radiogenomicsrespectively](https://wiki.cancerimagingarchive.net/display/Public/NSCLC+Radiogenomicsrespectively)). [PERSON] and [PERSON] conceived and designed this study. [PERSON] performed the experiments, simulations, and original draft preparation of the paper. [PERSON], [PERSON] and [PERSON] reviewed and edited the paper. This research received no external funding. The authors express sincere gratitude to the ASEAN University Network/Southeast Asia Engineering Education Development Network (AUN/SEED-Net) and the Japan International Cooperation Agency (JICA) for financial support. We would also like to thank The Cancer Imaging Archive (TCIA) for publically sharing the CT scan images and clinical data which were applied in our experiments. The authors declare no conflict of interest.