EARLY LUNG CANCER SCREENING: A COMPARATIVE STUDY OF CNN AND RADIOMICS MODELS WITH PULMONARY NODULE BIOLOGIC CHARACTERIZATION

Abstract

Lung cancer has become an increasingly prevalent disease, with an estimated 125,070 deaths in the United States alone in 2024 ( 5). To improve patient outcomes and assist doctors in differentiating between benign and malignant pulmonary nodules, this paper developed a Convolutional Neural Network (CNN) model for early binary detection of pulmonary nodules and assessed its effectiveness compared to other approaches. The CNN model showed an accuracy of 98.47%, while the radiomics-based SVM-LASSO model and the Lung-RADS system showed accuracies of 84.6% and 72.2% respectively. This demonstrates that the CNN model is significantly more effective for the early binary detection of pulmonary nodules than both the radiomics-based model and the Lung-RADS system. The paper also discusses the applications of Deep Learning in healthcare, concluding that although AI proves to be an effective method for early lung cancer detection, more research is needed to carefully assess the role and impact of AI in healthcare.