# A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection
###### Abstract
These AI-based systems can aid in the initial diagnosis, highlighting situations that require further study by a pathologist, or as a confirmatory diagnostic tool, potentially reducing the time spent by pathologists on these tasks and enabling quicker progress towards a definitive diagnosis and subsequent therapy [9]. Proposals for AI-based systems aimed at improving cancer diagnosis, such as breast, brain, and skin cancer diagnosis, often employ deep learning algorithms known as convolutional neural networks (CNNs) [14; 15; 16; 17]. CNNs are designed to recognize complex patterns and features in images, making them an ideal tool for image classification, segmentation, and detection. The capacity of CNNs to learn from and perform better with additional data makes them extremely adaptable to different datasets and imaging modalities, which is one of their main advantages [18]. Recent research has shown that CNNs have the potential to increase the precision of medical image processing. For instance, in 2019, [PERSON] and his team developed a CNN algorithm that achieved an AUC (area under the curve) of 94.4% in detecting lung cancer in chest radiographs, outperforming six human radiologists [19]. However, despite their promising results, CNNs have some limitations in medical imaging,  
Figure 1: Actual approach for lung cancer diagnosis. (**a**) Perform a radiologic technique; (**b**) if suspected tumor tissue, proceed to the biopsy; (**c**) perform the biopsy extracting suspected tissues; (**d**) diagnosis executed by the pathologists through a brightfield microscope or whole-slide image (WSI); (**e**) communicate the exam result to the patient. including the need for large amounts of labeled data and potential biases in the training data [18]. The availability of clinical data on WSIs is increasing with the emergence of whole-slide imaging, which provides a means to develop systems that can perform tasks such as cancer detection and classification, overcoming the constraints described above, i.e., reducing the time spent by the pathologists in tasks related to tumor detection and classification and improving the accuracy of a cancer diagnosis [8; 9]. In recent years, several studies have been conducted to evaluate the performance of various AI-based approaches for lung cancer detection using WSIs (Table 1). In this context, notable approaches have been described in the literature. [PERSON] and colleagues released a publication in 2018 where they describe the development of a CNN based on the inception V3 algorithm to detect and classify ADC WSIs. The model used a sliding grid mechanism that traveled along the WSI in patches of \(300\times 300\) pixels, identifying each pixel as a nucleus centroid, non-nucleus, or nucleus boundary. They then extracted the geographic distribution in the tumor microenvironment, nuclear morphology, and textural aspects from the tumor site and used them as predictors in a recurrent cancer prediction model. The model achieved an accuracy of 89.8% from training and validation sets consisting of 267 and 457 images, respectively [20]. [PERSON] and colleagues employed a similar architecture to [PERSON]'s work, but with larger patch sizes of 512 \(\times\) 512 pixels. In addition to ADC samples, they included non-malignant and SCC samples, resulting in a dataset with 1635 slides. The team achieved an average area under the curve (AUC) of 0.97, suggesting that utilizing larger patch sizes and incorporating a variety of sample types may improve the performance of deep learning models in analyzing medical images [21]. [PERSON] and colleagues analyzed annotated WSIs from 712 patients using seven different architectures. Among the tested networks, UNet achieved the highest performance with a precision and F1-score of 0.80 and a recall of 0.86 [22]. [PERSON] and colleagues investigated the performance of four different neural network architectures, AlexNet, VGG, ResNet, and SqueezeNet, for analyzing lung WSIs from a small dataset of 33 patients.