A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection

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.

Automated summary

Lung cancer is a leading cause of cancer-related deaths worldwide, and developing computer-aided diagnosis systems can improve accuracy and reduce workload. This study developed a learning algorithm (CancerDetecNN) to evaluate tumor presence in lung whole-slide images (WSIs) with reduced computational cost.