Analysis of the Diagnosis Model of Peripheral Non-Small-Cell Lung Cancer under Computed Tomography Images.

Zhonghai Xie,Huaizhong Zhang

doi:10.1155/2022/3107965

Abstract

This study aimed to explore the effect of deep learning models on lung CT image lung parenchymal segmentation (LPS) and the application value of CT image texture features in the diagnosis of peripheral non-small-cell lung cancer (NSCLC). Data of peripheral lung cancer (PLC) patients was collected retrospectively and was divided into peripheral SCLC group and peripheral NSCLC group according to the pathological examination results, ResNet50 model and feature pyramid network (FPN) algorithm were undertaken to improve the Mask-RCNN model, and after the MaZda software extracted the texture features of the CT images of PLC patients, the Fisher coefficient was used to reduce the dimensionality, and the texture features of the CT images were analyzed and compared. The results showed that the average Dice coefficients of the 2D CH algorithm, Faster-RCNN, Mask-RCNN, and the algorithm proposed in the validation set were 0.882, 0.953, 0.961, and 0.986, respectively. The accuracy rates were 88.3%, 93.5%, 94.4%, and 97.2%. The average segmentation speeds in lung CT images were 0.289 s/sheet, 0.115 s/sheet, 0.108 s/sheet, and 0.089 s/sheet. The improved deep learning model showed higher accuracy, better robustness, and faster speed than other algorithms in the LPS of CT images. In summary, deep learning can achieve the LPS of CT images and show excellent segmentation efficiency. The texture parameters of GLCM in CT images have excellent differential diagnosis performance for NSCLC and SCLC and potential clinical application value.

Highlights

Due to the continuous expansion of the number of smokers and the increasingly serious environmental pollution, the incidence of lung cancer in recent years has shown an increasing trend year by year
An algorithm for lung parenchymal segmentation (LPS) of lung CT images was proposed based on a deep learning model to improve the efficiency of clinical diagnosis of peripheral non-small-cell lung cancer (NSCLC) and peripheral SCLC, and it was trained and verified. e CT images of patients with clinical peripheral NSCLC and peripheral SCLC were undertaken as objects, and texture features were extracted based on the images after LPS, so as to compare and analyze the differences in the texture features of CT images of patients
Verification of LPS Based on Convolutional Neural Networks (CNNs). e effect of lung segmentation using the Mask-RCNN model is shown in Figure 6. e red box in the figure represented the area where the model was segmented

Summary

Introduction

Due to the continuous expansion of the number of smokers and the increasingly serious environmental pollution, the incidence of lung cancer in recent years has shown an increasing trend year by year. An algorithm for LPS of lung CT images was proposed based on a deep learning model to improve the efficiency of clinical diagnosis of peripheral NSCLC and peripheral SCLC, and it was trained and verified. E CT images of patients with clinical peripheral NSCLC and peripheral SCLC were undertaken as objects, and texture features were extracted based on the images after LPS, so as to compare and analyze the differences in the texture features of CT images of patients In this way, the quantitative information used in the differential diagnosis of peripheral NSCLC in CT images was excavated to provide a basis for follow-up clinical precision treatment

Basic eories for Auxiliary Diagnosis of Peripheral NSCLC

Experimental Methods

Experimental Results and Analysis