Segmentation of lung computed tomography images based on SegNet in the diagnosis of lung cancer

Abstract

ABSTRACT Objective: To apply SegNet approach to establish an auxiliary diagnosis model for lung cancer based on lung computed tomography (CT) image scores, and to explore its value in distinguishing benign and malignant lung CT images. Methods: We selected 240 patients, half of whom were diagnosed as early-stage lung cancer, and half were diagnosed as benign lung nodules. This paper proposes a based on SegNet recognition technology to segment images, and compares the sensitivity, specificity, accuracy, total image segmentation time, and overlap rate of Deeplab v3, VGG 19 and manual image segmentation for lung cancer. Results: The overlap rate of the SegNet model is 95.11%, and the overlap rate closest to manual segmentation is 95.26%. The overlap rate of Deeplab v3 and VGG 19 is much lower than that of manual segmentation. The SegNet model has a sensitivity of 98.33%, a specificity of 86.67%, an accuracy of 92.50%, and a total segmentation time of 30.42 s, which is shorter than manual segmentation. Conclusion: Based on SegNet recognition technology, it can effectively improve the diagnostic sensitivity of early lung cancer, and assist physicians to screen early lung cancer more effectively and quickly, which is worthy of clinical promotion.