# Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer
## 3 Experiments and Results
### Performance Evaluation and Comparison  
We compute numerous performance parameters to assess the effectiveness of the proposed lung nodule detection algorithm. There are four possible outcomes when an image is tested with any lung nodule detection algorithm: if the image is cancerous and is predicted as cancerous, it is a true positive (\(TP\)); if the image is non-cancerous and is predicted as non-cancerous, it is a true negative (\(TN\)); if the image is non-cancerous but is predicted as cancerous, it is a false positive (\(FP\)); and if the image is cancerous but is predicted as non-cancerous, it is a false negative (\(FN\)). Based on this formation, we calculate the accuracy, sensitivity or recall, and precision values to test the performance of the proposed method. Accuracy reflects all true positive and true negative results in detecting nodules. It calculates the percentage of accurately predicted data from all data points as follows:  
\[Accuracy=\frac{TN+TP}{TN+TP+FN+FN} \tag{5}\]  
Precision reflects all positive results that are actually positive marked by the radiologist. \[Precision=\frac{TP}{TP+FP} \tag{6}\]  
Sensitivity or recall measures the percentage of successful results to determine the correct prediction. \[Recall=\frac{TP}{TP+FN} \tag{7}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Property** \\ \hline Number of CT scans & 888 \\ Annotated lung nodule & 1186 \\ Format & 3D MHD \\
2D slice size & \(512\times 512\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: LUNA16 dataset description. Figure 9: A 3D representation of a sample MHD image from LUNA16 dataset. We computed all performance metrics on the test dataset and compared the results with existing similar approaches. Specifically, we selected the following methods for performance comparison: [PERSON] [43], [PERSON] [44], [PERSON] [19], [PERSON] [45], [PERSON] [31], [PERSON] [46], and [PERSON] [47]. The method proposed by [PERSON] et al. [43] employed an artificial neural network to determine whether or not lung nodules were present in the CT image. The method presented in [44] used U-Net [24], encoder-decoder [48], and MixNet [49] models. In [PERSON] [31], 3D-CNN is used to detect lung nodules from CT scans. They preprocessed the images first using the watershed algorithm for lung segmentation. The lung nodule detector presented in [47] also uses a 3D-CNN model to identify lung nodules using low-dose CT scan images. In [19], the watershed algorithm is used to segment lungs from CT scans and feature extraction is used to detect nodules and then send these features to an SVM for classification. In [45], the authors used R-CNN and 2D CNN models to detect lung nodules and classify true nodules, respectively. The results of the proposed and the compared methods are presented in Table 2. The proposed algorithm achieves accuracy of 0.94, which is better than all compared methods except the Nasser [43] algorithm, which performs marginally better. However, they do not report the other metrics, making it impossible to draw a fair comparison. In terms of the precision and recall measures, the proposed algorithm outperforms the compared methods by achieving scores of 0.92 and 0.97, respectively. In recent years, deep-learning-based methods have been extensively explored in medical image analysis and showed good performance, particularly for image segmentation and classification problems. These approaches, however, require huge amounts of tagged data for training. Preparing ground truth for medical data is challenging because it needs expert radiologists and it is a very time-consuming task. Therefore, these approaches are not very practical. The proposed method does not require any such training and yet it can produce appreciable results. In Figure 10, we present a few results of the proposed algorithm on test images from the LUNA16 dataset. The results and statistics presented in Table 2 show that the proposed method is quite accurate and reliable.