Computed Tomography Images under Deep Learning Algorithm in the Diagnosis of Perioperative Rehabilitation Nursing for Patients with Lung Cancer

Abstract

This study aim was to explore the application effect of computed tomography (CT) image segmentation based on deep learning algorithm in the diagnosis of lung cancer. In this study, a two-dimensional (2D) convolutional neural network (CNN) and three-dimensional (3D) CNN fusion model was constructed firstly. Subsequently, 60 patients with lung cancer were randomly divided into a control group and an intervention group to receive perioperative routine nursing and rehabilitation nursing, respectively. The results revealed that the Dice value (0.876), sensitivity (0.849), and positive predictive value (PPV) (0.875) of the hybrid feature fusion model (HFFM) constructed in this study for lung cancer CT image segmentation were higher than those of other models, and the accuracy rate for lung cancer diagnosis was 96.7%. After nursing intervention, the partial arterial oxygen pressure (PaO2) and partial arterial carbon dioxide pressure (PaCO2) in the control group were 80.54 mmHg and 39.81 mmHg, respectively, while those in the intervention group were 83.09 mmHg and 36.75 mmHg, respectively. After intervention, the maximal voluntary ventilation (MVV) %, forced vital capacity (FVC) %, and forced expiratory volume in 1 sec (FEV1) %) in the intervention group were 76.03%, 82.14%, and 89.76%, respectively. It suggested that compared with the control group, the pulmonary function indexes of the intervention group improved significantly after nursing intervention ( $P$  < 0.05). In summary, the HFFM constructed in this study can be used for segmentation and classification of CT images of lung cancer patients, which can improve the accuracy of diagnosis and help improve the lung function and quality of life of patients.