Machine Learning Assisted Differential Diagnosis of Pulmonary Nodules Based on 3D Images Reconstructed From CT Scans

Abstract

Lung cancer is one of the most common and deadly diseases worldwide. The precise diagnosis of lung cancer at an early stage holds particular significance, as it contributes to enhanced therapeutic decision-making and prognosis. Despite advancements in computed tomography (CT) scanning for the detection of pulmonary nodules, accurately assessing the diverse range of pulmonary nodules continues to pose a substantial challenge. Herein, we present an innovative approach utilizing machine learning to facilitate the accurate differentiation of pulmonary nodules. Our method relies on the reconstruction of three-dimensional (3D) lung models derived from two-dimensional (2D) CT scans. Inspired by the successful utilization of deep convolutional neural networks (DCNNs) in the realm of natural image recognition, we propose a novel technique for pulmonary nodule detection employing DCNNs. Initially, we employ an algorithm to generate 3D lung models from raw 2D CT scans, thereby providing an immersive stereoscopic depiction of the lungs. Subsequently, a DCNN is introduced to extract features from images and classify the pulmonary nodules. Based on the developed model, pulmonary nodules with various features have been successfully classified with 86% accuracy, demonstrating superior performance. We hold the belief that our strategy will provide a useful tool for the early clinical diagnosis and management of lung cancer.