# A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection
## 3 Results and Discussion
### Evaluating the Performance of CancerDetecNN  
After analyzing and discussing the results of the existing CNNs on the acquired dataset, it was necessary to train CancerDetecNN Versions in order to evaluate and compare their performance. To ensure clarity and focus, this paper will limit its scope to a selected group of the highest-performing versions of CancerDetecNN. Specifically, the four versions that demonstrated the most consistent and greatest outcomes were identified and will therefore be the center of attention of this study's analysis (Table 8). The four versions selected for assessment were 5, 6, 7, and 12. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Epochs** & **CNN** & **Accuracy** & **Precision** & **Recall** & **AUC** & **F1-Score** & **Training** \\ \hline \multirow{3}{*}{150} & AlexNet & 0.821 & 0.885 & 0.815 & 0.836 & 0.849 & 2 h 22 min \\  & ResNet-50 Pre-trained & 0.815 & 0.854 & **0.859** & 0.825 & 0.857 & 2 h 37 min \\
200 & GoogLeNet & 0.823 & 0.861 & 0.841 & 0.841 & 0.851 & 2 h 07 min \\
250 & ResNet-50 & **0.839** & **0.894** & 0.836 & **0.856** & **0.864** & 3 h 23 min \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Best results achieved by each existing convolutional neural network. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Epochs** & \begin{tabular}{c} **CancerDetecNN** \\ **Version** \\ \end{tabular}  & **Accuracy** & **Precision** & **Recall** & **AUC** & **F1-Score** \\ \hline \multirow{3}{*}{100} & 5 & 0.861 & 0.960 & 0.803 & 0.927 & 0.874 \\  & 6 & 0.839 & 0.918 & 0.805 & 0.901 & 0.858 \\  & 7 & 0.849 & **0  
Discussing the accuracy results presented by the training of the four CancerDetecNN versions, it is possible to observe that they range from 0.761 to 0.885, with the fifth version achieving the highest score at 200 epochs. It is possible to note that the two lowest values (0.761 and 0.766) were both achieved by Version 12 at 100 and 250 epochs, respectively. Compared to the other versions, the lower accuracy of Version 12 could be attributed to the fact that the final max pooling layer is followed by a global average pooling layer. This could lead to a loss of spatial resolution and potentially result in the loss of information. The precision scores of the CancerDetecNN model were highest (0.983) and lowest (0.871) for Version 7 at 50 and 250 epochs, respectively. However, it is valuable to take the recall scores into account when assessing the overall effectiveness of the model. According to the values outputted, it is possible to state that all versions identified the negative samples correctly most of the time. Version 7 showed a trade-off between precision and recall, with the lowest precision and highest recall score of 0.863 when trained with 250 epochs. Version 12 had the lowest recall score of 0.626 at 100 epochs, indicating that the model failed to identify several positive cases. The AUC metric scores range mainly from 0.890 to 0.930, indicating that all versions of the CancerDetecNN model distinguish positive and negative samples well. It is notable that the highest AUC score of 0.930 was achieved by Version 7 at 100 epochs, while the lowest score of 0.830 was obtained using Version 12 at 250 epochs. Comparing the F1-score values, Versions 5 and 6 obtained the highest score of 0.897, although at different epochs (200 and 300, respectively). This indicates that they achieved a balance between precision and recall, which is essential for medical image analysis tasks where both false positives and false negatives can have severe consequences, such as patients undergoing unnecessary further treatments that could have negative effects on their health and quality of life or delaying the correct diagnosis and required treatment. Comparing Training Time of CancerDetecNNs for Optimal Performance  
Upon reviewing the results, the versions of CancerDetecNN that displayed the most superior and reliable metrics outcomes were chosen for comparison in terms of the time required to train each CNN until it achieved its optimal values (Table 9).