Lung-RetinaNet: Lung Cancer Detection Using a RetinaNet With Multi-Scale Feature Fusion and Context Module

Lung cancer is one of the terrible diseases in various countries around the globe, and timely detection of the illness is still a challenging process. The oncologists consider the blood test results and CT scans to assess the tumor, which is time-consuming and involves extra human effort. Therefore, an automated system should be developed to efficiently recognize lung tumors and assess their severity to reduce mortality. Although various researchers have proposed lung disease detection systems, the existing techniques still lack significant detection accuracy for early-stage tumors. Thus, this study proposes a novel and efficient lung tumor detector based on a RetinaNet, namely Lung-RetinaNet. A multi-scale feature fusion-based module is introduced to aggregate various network layers, simultaneously increasing the semantic information from the shallow prediction layer. Moreover, a dilated and lightweight algorithm is employed for the context module to combine contextual information with each network stage layer to improve features and effectively localize the tiny tumors. The proposed methodology attained 99.8% accuracy, 99.3% recall, 99.4% precision, 99.5% F1-score, and 0.989 Auc. We evaluated our suggested model and matched the performance with state-of-the-art DL-based methods. The outcomes show that our technique provides more substantial results than existing methods.