## 4 Conclusions  
In this paper, we presented a fully automated nodule detection for lung cancer diagnosis. Firstly, we proposed an automated seeded region growing (SRG) to segment the lungs from the input CT volume images. The strength of the proposed SRG is that it can select initial seed regions automatically and perform the segmentation without requiring homogeneity criteria to determine the similarity between the pixels. Secondly, we designed a 3D chain code algorithm for the detection of juxta-pleural nodules. It traces the voxelized boundary of the segmented lungs to identify the concave regions along the border and enclose them by detecting the inflection points. Compared to the 2D border reconstruction methods, the use of 3D chain coding takes less effort and provides more accurate results. Moreover, it does not require training data or other classification algorithms to find the start-point and end-point of the nodule gap. Finally, we developed an optimized random forest (RF) classifier using four attribute selection algorithms, namely, RELIEFF, genetic algorithm (GA), particle swarm optimization (PSO), and firefly algorithm (FA), for false reduction. Our proposed nodule detection was evaluated using 888 exams of LIDC-IDRI, and a favorable performance of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity with only 0.0863 false positives per scan was achieved using optimized random forest by firefly algorithm. Moreover, our proposed nodule detection ensures the detection of variant type and size of nodules using a standard machine. Therefore, it can be applied as a supportive tool for physicians and radiologists to diagnose lung cancer. However, as a limitation, the preliminary screening of our nodule detection depends on some empirical threshold values to reduce false positives. In future work, the pulmonary nodules detected by our proposed method can be further classified into different clinical stages to determine the severity of lung cancer. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Exams**} & **Acc** & **Sen** & **Spec** & **FP** \\  & & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(per exam)** \\ \hline \multirow{4}{*}{[13]} & \(\bullet\) & Global optimal & & & & & \\  & \(\bullet\) & active contour, & & & & & \\  & \(\bullet\) & Thresholding & & & & & \\  & \(\bullet\) & Morphological operations & 71 & 93.6 & 89.3 & - & 2.1 \\  & \(\bullet\) & Labeling & & & & & \\  & \(\bullet\) & SVM & & & & & \\ \hline \multirow{4}{*}{[24]} & \(\bullet\) & Candidate detectors & & & & & \\  & \(\bullet\) & CNN & 888 & - & 90.1 & - & 4 \\ \hline \multirow{4}{*}{[25]} & \(\bullet\) & Icosahedron sampling & & & & & \\  & \(\bullet\) & Thresholding based on & & & & & \\  & & nodule radii & 744 & 93.1 & - & - & - \\  & \(\bullet\) & CNN + FV features & & & & & \\  & \(\bullet\) & SVM & & & & & \\ \hline \multirow{4}{*}{Proposed Method} & \(\bullet\) & Automated seeded & & & & & \\  & & region growing & & & & & \\ \cline{1-1}  & \(\bullet\) & 3D chain code & 888 & 93.11 & 94.86 & 91.37 & 0.0863 \\ \cline{1-1}  & \(\bullet\) & Preliminary screening & & & & & \\ \cline{1-1}  & \(\bullet\) & RF + FA & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed this study; [PERSON] performed the experiments, simulations, and draft preparation of the paper; [PERSON], [PERSON], [PERSON] and [PERSON] reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Acknowledgments:** The authors' sincere thanks go to the National Cancer Institute, the Foundation for the National Institutes of Health, and The Cancer Imaging Archive (TCIA) for publicly sharing of thoracic CT scans that are applied in this research.