Adoption of computerized tomography images in detection of lung nodules and analysis of neuropeptide correlative substances under deep learning algorithm

To solve the problems of low sensitivity and high false positive rate of current lung nodule detection technology, and to explore the correlation between neuropeptide correlative substances and lung nodules, in the study, the deep learning-based computerized tomography (CT) images were used to detect lung nodules. Specifically, a multi-scale three-dimensional convolutional neural network (CNN) was established for lung nodule detection. First, high-quality candidate nodules were obtained by a two-dimensional CNN. Then, three-dimensional CNNs of different scales were constructed for candidate nodules of different sizes, and the fused model was used to classify candidate nodules. Finally, the detection results of the single network, the two-dimensional CNN, and the fused network were compared. Meanwhile, the serum levels of vasoactive intestinal peptide (VIP) and substance P (SP) were detected in patients with lung injury caused by lung nodules, so as to analyze the correlation between neuropeptide correlative substances and lung nodules. The results showed that the detection sensitivity of the fused network was 85.8% and 92.9% when the number of false positives was 1 and 4, respectively, which were higher than that of the single network and two-dimensional CNN. However, the content of VIP in the serum of patients with pulmonary nodules was significantly reduced, while the content of SP was significantly increased. Therefore, the algorithm proposed in this study can effectively improve the sensitivity of the pulmonary nodule detection system and reduce false positive rate; at the same time, neuropeptide correlative substances may be correlated with the lung injury caused by pulmonary nodules.