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

Sha Yan,Zhenxing Liu,Siying Yu,Qin Huang,Gustavo Ramirez

doi:10.1155/2022/8685604

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.

Highlights

Lung cancer is the most common malignant tumor that causes human deaths, and its incidence is increasing year by year [1,2,3]
computer technology (CT) Segmentation of Lung Cancer Based on Deep Learning. e current methods used for medical image segmentation processing are mainly divided into techniques based on manual features and depth features [13]. e convolutional neural network (CNN) model has been widely used in image classification and segmentation and it is mainly based on the image block training model to classify the central pixel [14]
Dice, sensitivity, and PPV were selected to perform quantitative evaluation of 2D CNN [18], 3D CNN [19], U-Net [20], and the model proposed in this paper. e results is shown in Figure 4. e accuracy of 2D CNN, 3D CNN, and U-Net was 80.0%, 85.0%, and 91.7%, respectively

Summary

Introduction

Lung cancer is the most common malignant tumor (i.e., the main malignant tumor) that causes human deaths, and its incidence is increasing year by year [1,2,3]. E era of artificial intelligence has come, and the development of deep learning technology has given birth to various developments. In this context, some scholars have proposed imaging omics to extract high-throughput omics features from cancer medical images. E features that doctors can see from the image data with naked eyes, such as shape features, tumor size, and wavelet transform, can have a good predictive effect through deep learning technology modeling. With the increase in the incidence of lung cancer, the CT image information of patients shows an exponential growth trend, so it is very important to accurately identify lung nodules from CT images [9, 10]. Medical image analysis is greatly affected by subjective experience. e use of computer technology (CT)

Methods

Results

Conclusion