## 4 Experimental Results
For graphical representation, box-plot distributions were applied to compare the performance of five alternative classifiers. Figure 14 illustrates the box-plots comparison between mean performance measurements generated by each classifier in first stage classification. Similarly, Figure 15 shows the box-plots comparison for second stage classification. Analyzing the distributions of box-plots, we can know that BPNN offers significantly higher performance than other classifiers in first stage classification. However, for the second stage classification, the performance of classifiers declined compared to the first stage because of class imbalance in the samples. The performance of DT, KNN and SVM decreased, and it indicates that they are not robust enough for random data with class imbalance. On the other hand, the ET and BPNN are robust for random data. The performance of ET seemed better than BPNN especially in specificity and precision because its box-plots had upper middle lines (higher median values) and small range of whiskers (condense data). But in other assessments such as accuracy, sensitivity, F1-score and AUC, BPNN showed higher values. Therefore, we decided to use BPNN for both stages of classification. As a result, BPNN reached an accuracy of 92.8%, a sensitivity of 93.6%, a specificity of 91.8%, a precision of 91.8%, an F1-score of 92.8% and an AUC of 96.8% for detection. For staging, it achieved an accuracy of 90.6%, a sensitivity of 77.4%, a specificity of 97.4%,  
precision of 93.8%, an F1-score of 79.6% and an AUC of 84.6% respectively. Even though BPNN took more execution time, it can guarantee random and imbalanced samples. As an example, the outputs of our proposed CAD are illustrated in Figure 12:1 They demonstrate 3D visualizations of tumors of different stages. Figure 16a represents a stage T1a tumor that is 1.83 cm in its greatest dimension and is located in the left lower lobe in an isolated manner. Figure 16b demonstrate a stage T1b tumor which is also located in the left lower lob, but is bigger in size (2.14 cm) than the tumor in Figure 16a. Figure 12:2 First stage classification: Box-plot distributions of mean performance measurements for five alternative classifiers using repeated random trials. Figure 12:3 Second stage classification: Box-plot distributions of mean performance measurements for five alternative classifiers using repeated random trials. Then, Figure 16c,d represents stage T2a and T2b tumors, respectively. Both tumors are located in the right upper lobes. The tumor in Figure 16c is 3.05 cm in its greatest dimension and is isolated, whereas the tumor in Figure 16d is 2.10 cm and touches the visceral pleural. Hence, the tumor in Figure 16d has a higher stage even though is smaller in size. Figure 16e,f illustrates the stage T3 tumors. The tumor in Figure 16e is located in the right upper lobe and invades the chest wall. Also, Figure 16f demonstrates the presence of satellite nodules in the same lobe, the right upper lobe. Unlike Figure 16f, the tumors in Figure 16g contain satellite nodules in different lobes: one is in the right middle lobe, and another is in the left upper lobe. Thus, it is classified as stage T4. Finally, in Figure 16h, we can see a T4-staged tumor nodule which invades the mediastum. Moreover, we compared the performance of our proposed CAD with methods presented in previous works, as stated in Figure 12:4 It was difficult to make a fair comparison because the CADs were developed using different datasets, different numbers of samples, and different methods. Thus, we could only make a quantitative comparison of the performance outcomes.