Abstract
This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical system and patients. A lung computed tomography (CT) image segmentation algorithm was constructed based on the deep learning convolutional neural network (CNN). The clinical data of 69 patients with lung nodules diagnosed by needle biopsy and pathological comprehensive diagnosis at hospital were collected for specific analysis. The CT image segmentation algorithm was used to distinguish the nature and volume of lung nodules and compared with other computer aided design (CAD) software (Philips ISP). 69 patients with lung nodules were treated by radiofrequency ablation (RFA). The results showed that the diagnostic sensitivity of the CT image segmentation algorithm based on the CNN was obviously higher than that of the Philips ISP for solid nodules <5 mm (63 cases vs. 33 cases) (P < 0.05); it was the same result for the subsolid nodule <5 mm (33 case vs. 5 cases) (P < 0.05) that was slightly higher for solid and subsolid nodules with a diameter of 5–10 mm (37 cases vs. 28 cases) (P < 0.05). In addition, the CNN algorithm can reach all detection for calcified nodules and pleural nodules (7 cases; 5 cases), and the diagnostic sensitivities were much better than those of Philips ISP (2 cases; 3 cases) (P < 0.05). Patients with pulmonary nodules treated by RFA were in good postoperative condition, with a half-year survival rate of 100% and a one-year survival rate of 72.4%. Therefore, it could be concluded that the CT image segmentation algorithm based on the CNN could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules.
Highlights
Xixi Guo,1 Yuze Li,2 Chunjie Yang,3 Yanjiang Hu,3 Yun Zhou,3 Zhenhua Wang,1 Liguo Zhang,1 Hongjun Hu,1 and Yuemin Wu 3
Patients with pulmonary nodules treated by radiofrequency ablation (RFA) were in good postoperative condition, with a half-year survival rate of 100% and a one-year survival rate of 72.4%. erefore, it could be concluded that the computed tomography (CT) image segmentation algorithm based on the convolutional neural network (CNN) could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules
A segmentation algorithm based on the CNN was proposed to segment the lung CT images of patients with lung nodules, which improved and optimized the diagnosis of lung nodules in lung CT images, showing relatively high robustness. erefore, it could be well adapted to the lung CT images obtained by multiple devices
Summary
Xixi Guo ,1 Yuze Li ,2 Chunjie Yang ,3 Yanjiang Hu ,3 Yun Zhou ,3 Zhenhua Wang ,1 Liguo Zhang ,1 Hongjun Hu ,1 and Yuemin Wu 3. Erefore, it could be concluded that the CT image segmentation algorithm based on the CNN could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules. CT uses computer technology to divide the human body from the horizontal level by pixels It can detect the relative densities of different parts by changing the position of the X-ray and the orientation of the detector receiving the signal according to the strength displayed on the detector, further displaying the human body scan image [5]. Compared with X-ray examination, CT examination can detect lung nodules with a smaller volume, which helps medical staff to correctly diagnose lung nodules and adopt reasonable treatment methods [6]
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