A novel two-stage deep learning approach for lung cancer using enhanced ResNet50 segmentation and LungSwarmNet classification

One of the leading killers globally now is lung cancer. It ranks high among the dangerous malignant tumors that people may have. It is the leading cause of cancer-related fatalities in both men and women globally, and its mortality rate is higher than that of any other malignant tumor. Medical image segmentation has seen the successful use of many deep learning framework-based techniques in recent years. The use of computer tomography (CT) has increased in the detection of lung cancer, a significant malignancy. Patients with lung cancer have a better chance of surviving if the disease is detected early. Early diagnosis allows professionals to deliver appropriate therapy within a specific period, which in turn reduces the fatality rate. The healthcare industry benefits greatly from the advanced services deep learning models offer. In this study, we present a deep neural network architecture named as lung swarm net that combines DenseNet201 with PSO to classify lung cancer from CT scans of the lung. We used ResNet50 for the segmentation procedure and DenseNet-201 with Particle Swarm Optimization (PSO) for the classification in order to identify lung cancer. According to the experimental data, the suggested model outperforms other current models in terms of accuracy.