# A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection
###### Abstract  
Lung cancer is a leading cause of cancer-related deaths worldwide, and its diagnosis must be carried out as soon as possible to increase the survival rate. The development of computer-aided diagnosis systems can improve the accuracy of lung cancer diagnosis while reducing the workload of pathologists. The purpose of this study was to develop a learning algorithm (CancerDetecNN) to evaluate the presence or absence of tumor tissue in lung whole-slide images (WSIs) while reducing the computational cost. Three existing deep neural network models, including different versions of the CancerDetecNN algorithm, were trained and tested on datasets of tumor and non-tumor tiles extracted from lung WSIs. The fifth version of CancerDetecNN (CancerDetecNN Version 5) outperformed all existing convolutional neural network (CNN) models in the provided dataset, achieving higher precision (0.972), an area under the curve (AUC) of 0.923, and an F1-score of 0.897, while requiring 1 h and 51 min less for training than the best compared CNN model (ResNet-50). The results for CancerDetecNN Version 5 surpass the results of some architectures used in the literature, but the relatively small size and limited diversity of the dataset used in this study must be considered. This paper demonstrates the potential of CancerDetecNN Version 5 for improving lung cancer diagnosis since it is a dedicated model for lung cancer that leverages domain-specific knowledge and optimized architecture to capture unique characteristics and patterns in lung WSIs, potentially outperforming generic models in this domain and reducing the computational cost. Artificial intelligence; computer-aided diagnosis; convolutional neural networks; deep learning; early-stage detection; histopathology; lung cancer; medical imaging +
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The process for the detection begins with radiologic techniques such as a chest X-ray, computed tomography, or magnetic resonance imaging (Figure 1a). If malignant tissue is suspected (Figure 1b), a biopsy is performed to obtain material for histopathological examination (Figure 1c). Formalin-fixed paraffin-embedded tissues are processed to create glass slides that are routinely stained with hematoxylin and eosin. Pathologists analyze these glass slides through either a brightfield microscope or a whole-slide image (WSI), which is a high-resolution digital scan of a microscopic slide that allows for counting, measuring the sizes and densities of objects, and applying image processing algorithms to detect lesions or cancer (Figure 1d). Finally, the exam results are communicated to the patient (Figure 1e) [3]. While the process seems simple, histological evaluation can be time-consuming, requires a high level of precision, and should be performed in the early stages of cancer to allow for more treatment options [8; 9]. Early detection is critical in improving treatment options and survival rates for lung cancer patients. Studies have consistently shown that individuals diagnosed with lung cancer at an early stage have a significantly better chance of survival than those diagnosed at a later stage [10; 11]. In fact, the five-year survival rate for lung cancer patients is up to 70% when the cancer is detected at an early stage before it has spread to other parts of the body [12]. However, despite the clear benefits of early detection, a significant proportion of lung cancer cases are still diagnosed at an advanced stage, when treatment options are limited, and the prognosis is poor. This is often due to the non-specific nature of these symptoms, which can delay diagnosis until the cancer has advanced. Therefore, there is an urgent need for effective screening programs that can detect lung cancer at an early stage. One promising approach is the integration of software that uses technologies such as artificial intelligence (AI) to improve the accuracy and efficiency of a cancer diagnosis [13].