Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks.

Background: Deep learning has shown considerable promise in the differential diagnosis of lung lesions. However, the majority of previous studies have focused primarily on X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), with relatively few investigations exploring the predictive value of ultrasound imaging. Objective: This study aims to develop a deep learning model based on ultrasound imaging to differentiate between benign and malignant peripheral lung tumors. Methods: A retrospective analysis was conducted on a cohort of 371 patients who underwent ultrasound-guided percutaneous lung tumor procedures across two centers. The dataset was divided into a training set ( Results: Among the five models, the one based on the ResNet18 algorithm demonstrated the highest performance. It exhibited statistically significant advantages in predictive accuracy ( Conclusion: The ResNet18-based deep learning model demonstrated superior accuracy in distinguishing between benign and malignant peripheral lung tumors, providing an effective and non-invasive tool for the early detection of lung cancer.