# A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection
###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Methods** & **Datasets** & **Results** \\ \hline [PERSON] et al. (2018) [20] & CNN based on inception & 267 training and 457 & Accuracy of 0.898 \\ [PERSON] et al. (2018) [20] & V3 algorithm using sliding & \begin{tabular}{c} validation ADC WSIs \\ \end{tabular} & Accuracy of 0.898 \\ \hline [PERSON] et al. (2018) [21] & CNN based on inception & 1635 slides (ADC, \\  & V3 algorithm using tiles & non-malignant, SCC) & Average AUC of 0.97. \\ \hline [PERSON] et al. (2018) [22] & Seven different & 712 annotated WSIs & Precision of 0.80, F1-score of 0.80, \\  & architectures including & 712 annotated WSIs & recall of 0.86. \\ \hline [PERSON] et al. (2018) [23] & \begin{tabular}{c} AUC results for training from scratch: \\ \end{tabular} \\  & \begin{tabular}{c} 0.912 (AlexNet), 0.911 (SqueezeNet), \\ \end{tabular} & 33 lung patients & \begin{tabular}{c} AUC results for fine tuning: \\ 0.891 (ResNet), 0.881 (VGGNet). \\ \end{tabular} \\  & and SqueezeNet CNNs & 33 lung patients & \begin{tabular}{c} AUC results for fine tuning: \\ 0.859 (AlexNet), 0.875 (SqueezeNet), \\ \end{tabular} \\  & & 0.869 (ResNet), 0.830 (VGGNet). \\ \hline [PERSON] et al. (2019) [24] & \begin{tabular}{c} Pre-trained CNNs \\ (AlexNet, VGG-16, \\ GoogLeNet, and \\ ResNet-50) using tiles of \\ 1000 \(\times\) 1000 pixels \\ \end{tabular} & 1314 ADC and SCC WSIs & AUC values detecting ACC: 0.890 for \\  & \
\begin{tabular}{c} AUC values detecting ACC: 0.890 for \\ AlexNet, 0.912 for VGG-16, 0.901 for \\ GoogLeNet, and 0.935 for ResNet-50. \\ AUC values detecting SCC: 0.995 for \\ AlexNet, 0.991 for VGG-16, 0.993 for \\ GoogLeNet, and 0.979 for ResNet-50. \\ \end{tabular} \\ \hline [PERSON] et al. (2019) [25] &
\begin{tabular}{c} CNNs VGG-16 and \\ ResNet-50 using patches \\ of 256 \(\times\) 256 pixels \\ \end{tabular} & 25 lung WSIs & ROC values of 0.833 (VGG-16) and \\  & & 0.796 (ResNet-50). \\ \hline [PERSON] and [PERSON] (2020) [26] & Custom CNN with tiles & 5000 histopathology images per category (benign, ADC, \\  & & & 0.96 (ADC); precision of 1.00, recall \\  & & & 1.00, F1-score of 1.00 (benign); \\  & & SCC) & precision of 0.97, recall of 0.95, F1-score of 0.96 (SCC). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Published studies on lung cancer detection using AI-based histopathological analysis. Based on the critical analysis of the previous studies, the authors of this study suggest an approach for the detection and diagnosis of tumors in WSIs. The proposed method involves splitting the WSI into patches of 512 \(\times\) 512 pixels and feeding them into a CNN that produces a heatmap indicating the presence or absence of the tumor (Figure 2) [3,9]. Recurring to this method, the split of the WSI into smaller patches enables the models to capture detailed information from the regions of interest, allowing for a more accurate diagnosis of a tumor area. The application of a CNN benefits the extraction of meaningful features from the input patches, making it possible to identify patterns indicative of tumor presence. The resulting heatmap generated by the CNN provides a visual representation to the pathologists, indicating the tumor distribution across the WSI. This approach is expected to significantly reduce the time required for pathologists to analyze WSIs, decrease healthcare costs, and enhance the accuracy of diagnosis. The open literature indicates that deep learning models have shown promising results in medical image analysis tasks, including breast cancer detection. As reviewed above, the studies provide solutions to identify the presence of a tumor in a lung WSI and/or classify it. However, there are gaps that need to be addressed, such as the fact that most of the authors in the field have utilized CNNs that have already been developed and tested on various image classification problems but did not create a specific model for lung cancer.