## 4 Experimental Results  
In this study, we propose a CAD that is capable of detecting and staging of the pulmonary tumors for lung cancer diagnosis. This CAD was developed and evaluated using four datasets, namely LIDC-IDRI [29; 30], NSCLC-Radiomics-Genomics [2; 31], NSCLC-Radiomics [2; 32], and NSCLC Radiogenomics [33; 34] (Supplementary Materials) where NSCLC stands for non-small cell lung cancer. These datasets are downloadable from The Cancer Imaging Archive (TCIA) [35] which is an open source of medical images for scientific and educational research. Expert radiologists confirmed all of the tumor nodules in these datasets, and the clinical diagnosis results were also provided. We applied these results as the gold standard to evaluate the performance of proposed CAD. Table 4 presents detailed descriptions of each dataset used in this study. As described earlier, our CAD has two primary purposes: detection and staging. The first purpose is more fundamental for the production of accurate diagnosis results because the correctly predicted tumor nodules from the first stage are further applied for staging. Among the datasets mentioned above, the LIDC-IDRI only provides XML files for the true nodule annotations. The clinical stages of the tumor nodules are not available in LIDC-IDRI. Therefore, we applied it only for the detection stage. Moreover, it is a huge dataset and had been employed by several researchers to develop CADs for nodule detection. There are 1010 exams in the LIDC-IDRI dataset, but only 888 exams were applied for our experiment because some exams contain missing and inconsistent slices. The application of LIDC-IDRI for the first purpose, detection, also acted as a separate validation for more robust detection. Unlike LIDC-IDRI, the three NSCLC datasets can provide the data for clinical stages of tumor. Thus, we applied these three datasets for the second purpose. Indeed, the CT exams from these three datasets had to pass through the first stage classification to segregate the true tumor nodules. Subsequently, the true predicted tumors were categorized into different clinical stages by the second-stage classifier. Therefore, the first stage of classification was double validated by not only LIDC-IDRI, but also NSCLC. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Features** & **Description** & **Returns** \\ \hline Invasion & \begin{tabular}{l} Determines the invasion of tumor nodules into different zones \\ and returns zone numbers. \\ Determines whether the tumor nodules attach the visceral \\ pleura or not and returns Boolean value (0 = not pleural \\ attachment and 1 = pleural attachment). \\ Count & \begin{tabular}{l} Calculates the number of true predicted tumor nodules in a \\ single exam and returns an integer value \(n\). \\ Determines which lobe the tumor nodules exist in and return \\ the lobe numbers. \\ \end{tabular} & \begin{tabular}{l} 1, 2, 3, 4, 5, 6, 7 \\ \end{tabular} \\ \begin{tabular}{l} Pleural attachment \\ \end{tabular} & \begin{tabular}{l} Determines whether the tumor nodules attach the visceral \\ pleura or not and returns Boolean value (0 = not pleural \\ attachment and 1 = pleural attachment). \\ \end{tabular} & 0,1 \\ Count & \
\begin{tabular}{l} Calculates the number of true predicted tumor nodules in a \\ single exam and returns an integer value \(n\). \\ Determines which lobe the tumor nodules exist in and return \\ the lobe numbers. \\ \end{tabular} &
\begin{tabular}{l} 1,..., n \\ 1, 2, 3, 4, 5 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Locational features. However, the second stage of classification is a multi-classification problem that outputs seven classes. Class 1 represents T1a, class 2 represents T1b, class 3 represents T2a, class 4 represents T2b, class 5 represents T3, class 6 represents T5, and class 7 represents T0/Tis.