# A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection
###### Abstract
The networks were trained using two approaches, from scratch and pre-trained, using input patches of 256 \(\times\) 256 pixels cropped with a stride of 196 pixels to ensure sufficient overlap between neighboring patches. Regarding the results for the AUC metric, when trained from scratch, AlexNet achieved the highest score of 0.9119, followed closely by SqueezeNet at 0.9113. ResNet and VGGNet achieved AUC values of 0.8912 and 0.8810, respectively. In contrast, when using pre-trained models, SqueezeNet achieved the highest AUC of 0.8749, followed by ResNet with 0.8687. AlexNet and VGGNet achieved lower AUC values of 0.8585 and 0.8300, respectively [23]. In 2019, [PERSON] and colleagues conducted a study where they utilized pre-trained CNNs, including AlexNet, VGG-16, GoogLeNet, and ResNet-50, from ImageNet to detect ADC and SCC. From a dataset of 1314 ADC and SCC WSIs, they used patches of 1000 \(\times\) 1000 pixels with a 50% overlap to train the models. The results for the AUC metric in the validation cohort showed that, when identifying ADC from adjacent dense benign tissues, AlexNet achieved 0.890, VGG-16 was able to obtain 0.912, GoogLeNet obtained 0.901, and ResNet-50 obtained 0.935. Additionally, when detecting SCC for adjacent dense benign tissues, the AUC values were 0.995 for AlexNet, 0.991 for VGG-16, 0.993 for GoogLeNet, and 0.979 for ResNet-50 [24]. [PERSON] and his team used 256 \(\times\) 256 pixels patches with a stride of 196 pixels to train the CNNs VGG-16 and ResNet-50. Their dataset was composed of tiles extracted from 25 lung WSIs, which resulted in receiver operating characteristic (ROC) values of 0.833 for VGG-16 and 0.796 for ResNet-50 [25]. In the next year, 2020, [PERSON] and [PERSON] developed their own CNN, which involved resizing each tile to 180 \(\times\) 180 pixels. Their dataset comprised tiles from three classes: benign tissue, ADC, and SCC, with 5000 histopathology images per category. The model achieved a precision of 0.95, a recall of 0.97, and an F1-score of 0.96 for adenocarcinoma; a precision of 1.00, a recall of 1.00, and an F1-score of 1.00 for benign tissue; and a precision of 0.97, a recall of 0.95, and an F1-score of 0.96 for SCC [26]. [PERSON] and his team proposed a novel approach in which the WSIs were used directly as an input for the ResNet-50 model, eliminating the need for pathologists to manually annotate the slides [3]. Due to memory constraints, they proposed using the WSI with a magnification of 4\(\times\) initially to discover the critical regions and, for the final identification, the 40\(\times\) magnification pictures of those regions. They tested their method on a dataset of 9662 WSIs and achieved AUC values of 0.9594 and 0.9414 for ADC and SCC, respectively [27]. In 2022, [PERSON] and colleagues developed a custom CNN and trained it with 5000 histopathology images per category (benign, ADC, and SCC). The ADC was detected with a precision of 0.933, a recall of 0.717, and an F1-score of 0.811. The benign category was identified with a precision of 1.00 and a recall score of 0.943, yielding an F1-score of 0.971. The SCC category was recognized with a precision of 0.774 and a recall of 0.998, obtaining an F1-score of 0.872 [28]. [PERSON] and his team published a paper in 2023 where they tested three pre-trained CNN models (GoogLeNet, ResNet-19, and ShuffleNet V2) and created their own CNN. The employed dataset had 15,000 images, with 5000 images in each group (benign, ADC, and SCC). The F1-score of the GoogLeNet was 0.96 for ADC, 1.00 for benign, and 0.97 for SCC. ResNet-18 produced an F1-score of 0.98 for ADC, 1.00 for benign, and 0.98 for SCC. The ShuffleNet V2 earned an ADC score of 0.96, a benign score of 1.00, and an SCC value of 0.96. The created CNN scored 0.89 for ADC, 0.98 for benign, and 0.91 for SCC [29].