## 5 Discussion  
In this research work, a modified gradient boosting classifier was proposed to identify pulmonary nodules as either benign, malignant, or non-nodule. The CT scan images  
Figure 8: (**a**) Precision vs. recall curve and (**b**) ROC curve for the proposed modified gradient boosting classifier model. Figure 7: Confusion matrix of the proposed modified gradient boosting classifier. obtained from the LIDC-IDRI dataset were pre-processed to remove artifacts using an anisotropic nonlinear diffusion filter, and it was observed that this provides a PSNR of 34.56 dB. This proves the efficacy of the experimented pre-processing method. The random walker method was used to extract the boundaries of the pulmonary nodules from the pre-processed CT scan images. The ground truth standard and the segmented result obtained using the random walker method was analyzed using DSC and JSC, which were found to be 0.979 \(\pm\) 0.011 and 0.877 \(\pm\) 0.008, respectively. The accuracy of the proposed research was compared with existing methods, such as the graph cut method, watershed method, 3D U-Net [36], and 3D FCN [37]. From Table 1, it can be seen that the pulmonary segmentation using the random walker method achieved the higher DSC and JSC, compared with other existing methods. The major contribution of the proposed research could segment the appropriate region of the pulmonary nodule for different cases. Various texture features were extracted from the segmented image to train and test the proposed modified gradient boosting classifier using the LBP filter and steerable Riesz wavelet coefficients. The steerable Riesz wavelet functions are steering in the direction of the local gradient energy, which is more suitable for analyzing and processing the texture of the CT images. The ROC accuracy, precision, recall, and F1 score were used to measure the performance of the proposed classifier. To confirm the superiority of the proposed modified gradient boosting classifier model, the performance was compared with the existing methods/models. The various methods/models considered were random forest classifier [38], the deep RNN model [39], the SVM classifier [16], and Res U-Net [36]. The performance comparison is shown in Table 2. From Table 1, it can be seen that the proposed modified gradient boosting classifier achieved a validation accuracy of 95.67%, which is higher than other reporting methods. The main reason for this is that the modified gradient boosting classifier algorithm uses a sequential approach, making it more accurate and efficient. Additionally, it achieved a higher precision and F1 score, which indicates that it is suitable for more efficient pulmonary nodule classification. However, from Figure 7, it can be seen that the proposed classifier drops in accuracy when a higher number of malignant nodules are presents in the testing image dataset. This is identified as one of the major limitation of the proposed classifier. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods/Model** & **DASC** & **JSC** \\ \hline Graph Cut Method & 0.566 \(\pm\) 0.025 & 0.414 \(\pm\) 0.021 \\ \hline Watershed Method & 0.628 \(\pm\) 0.027 & 0.494 \(\pm\) 0.025 \\ \hline
3D U-Net & 0.911 \(\pm\) 0.009 & 0.811 \(\pm\) 0.011 \\ \hline
3D FCN & 0.845 \(\pm\) 0.008 & 0.738 \(\pm\) 0.011 \\ \hline Proposed Method & 0.979 \(\pm\) 0.011 & 0.877 \(\pm\) 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Image segmentation performance comparison with existing methods.