## 5 Discussion and Conclusions  
This paper presents a CAD capable of the detection and staging of lung tumors from computed tomography (CT) scans. Our CAD has two major contributions. The first contribution is the extraction of locational features from tumor nodules for the T-staging of lung cancer. The second is the double use of the BPNN classifier for the detection and staging of the tumor nodules. The CAD works in three pivotal phases called segmentation, detection, and staging. A single CT exam of a patient containing multiple 2D slices is an input of the proposed CAD. From each input exam, the lung anatomical structures, such as the lung regions, trachea, bronchial airway tree, pulmonary vessels, fissures, lobes, and nodule candidates, are initially segmented using gray-level thresholding and isotropic interpolation methods. Subsequently, 28 nodule features are extracted from the segmented nodule candidates to detect the true tumor nodules. These features include four geometric, five intensity, and nineteen texture features. Using these features, the first stage of classification is performed and the true tumor nodules are segregated. Once the true tumor nodules have been achieved, the locational features are added for staging. With the help of the segmented lung anatomical structures from the first phase, the locational features can be defined by dividing the lungs into different zones. Moreover, the invasion and attachment of the tumor nodules to other pulmonary organs are also checked by using the 3D quick hull algorithm and logical array checking. As well as this, the number of true tumor nodules predicted in a single test and their lobe locations are also extracted in this stage. Using these features, the staging of tumor nodules \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CADs** & & **Methods** & **Dataset** & **Samples** & **Accuracy** \\ \hline \multirow{4}{*}{[10]} & \(\bullet\) & Gabor filter & & & \\  & \(\bullet\) & Marker-controlled watershed & & & \\  & \(\bullet\) & Geometric features (area, & & \\  & perimeter, eccentricity) & & & \\  & \(\bullet\) & SVM & & & \\ \hline \multirow{4}{*}{[11]} & \(\bullet\) & Sharpening & & & \\  & \(\bullet\) & Marker-controlled watershed & & & \\  & \(\bullet\) & Area, perimeter, eccentricity, & & & \\  & \(\bullet\) & convex area, mean intensity & & & \\  & \(\bullet\) & Thresholding & & & \\  & \(\bullet\) & Random tree & & & \\ \hline \multirow{4}{*}{[12]} & \(\bullet\) & Median filter & & & \\  & \(\bullet\) & Watershed transform & & & \\  & \(\bullet\) & Textural features & & & \\  & \(\bullet\) & Multi-class SVM classifier & & & \\ \hline \multirow{4}{*}{[14]} & \(\bullet\) & 3D bounding boxes (manual) & \(\bullet\) & Institutional dataset & \\  & \(\bullet\) & CNN & & (FDC-PET)CT & 472 & Staging—90\% \\ \hline \multirow{4}{*}{[15]} & \(\bullet\) & K-means clustering & & & \\  & \(\bullet\) & Double CDNN & & & \\ \hline \multirow{4}{*}{Proposed method} & \(\bullet\) & HU-based thresholding & & & \\  & \(\bullet\) & 3D nodule features (geometric, & & \\  & textural, intensity) and & & & \\  & locational features & & & \\  & \(\bullet\) & Double-staged BPNN & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Quantitative performance comparison of the proposed CAD method with methods presented in previous works. is conducted by second-stage classification. In this study, we applied the BPNN for both classifications. Before selecting the BPNN, we tested and compared it with five alternative classifiers using repeated random trials. Based on the comparative results, we determined that the BPNN should be used due to its high performance and robustness. Our proposed CAD was developed and tested using 1560 CT exams from four popular public datasets: LIDC-IDRI, NSCLC-Radiomics-Genomics, NSCLC-Radiomics and NSCLC Radiogenomics. In the performance evaluation, our proposed CAD achieved a detection accuracy of 92.8%, a sensitivity of 93.6%, a specificity of 91.8%, a precision of 91.8%, an F-score of 92.8%, and an AUC of 96.8%.