Spiral CT Image Characteristics and Differential Diagnosis Secondary Pulmonary Tuberculosis and Lung Cancer Based on Visual Sensors

Abstract

Helical CT plain scan has high spatial and area resolution, which is beneficial to the extraction of CT features of pulmonary nodules, and is of great significance for the diagnosis and differential diagnosis of pulmonary diseases. In order to deeply study the role of visual sensor image algorithm in CT image, this paper adopts clinical simulation method, data fusion method, and image acquisition method to collect images, analyze CT image features, and simplify the algorithm and create a CT model that can better diagnose secondary tuberculosis and lung cancer. We selected 45 patients with lung disease in this group, with an average age of 38 years. At the same time, the consistency analysis results of the diameter and plain CT value data of the five groups of cases measured by two observers are between 0.82 and 0.88, which has a good consistency. We could find that the nodule diameters of the five groups of cases were different ( $F$  =16.99, $P<0.01$ ), and the difference was statistically significant ( $P<0.06$ ), indicating that our data are not only accurate but also very reliable. ROC was used to analyze the precise value of CT values in the pulmonary tuberculosis group and lung cancer group, intrapulmonary lymph node group, and pulmonary hamartoma group to determine the cutoff value. The results showed that the AUC values of the pulmonary tuberculosis group and the lung cancer group were 0.788, and the middle was the largest, indicating that the values were guaranteed. The basic realization starts with visual sensor technology and designs a clinical model that can more accurately identify CT images and differential diagnosis.