四川大学学报（自然科学版）2024，Vol.61Issue(4)：145-154,10.DOI:10.19907/j.0490-6756.2024.042006
基于CT图像的深度神经网络肺功能预测
Deep neural networks based lung function prediction using CT images
摘要
Abstract
The latest epidemiological survey reveals a high and escalating prevalence rate of chronic respira-tory diseases,such as Chronic obstructive pulmonary disease(COPD)and asthma,posing a significant public health threat.Computer tomography(CT)has emerged as a convenient and noninvasive method for evaluat-ing pulmonary function,however,in existing computer-aided lung function evaluation method,handcrafted methods are insufficient and existing neural network methods are less effective in extracting features from small datasets with high noise and sparse data.In this study,a lung function prediction network(LFP-ResNet)is introduced to predict lung function from CT images.Firstly,a multi-level contextual feature fusion(MCFF)method is proposed to extract diverse features that represent the pulmonary texture and morphology effectively.Secondly,a three-dimensional(3-D)residual network is used to guarantee the spatial heterogene-ity of CT image sufficiently.Finally,a dataset containing both healthy population and patients with chronic re-spiratory diseases are constructed and used to compare the proposed methods with other state-of-the-art meth-ods.The results demonstrate that the proposed MCFF strategies are more efficient in extracting features from a sparse matrix with noise than other feature extraction methods.Moreover,the constructed LFP-ResNet ex-hibits better predictive performance in the pulmonary function prediction task.关键词
计算机断层扫描/深度学习/多任务学习/肺功能检查/慢性阻塞性肺疾病Key words
Computed tomography/Deep learning/Multi-task learning/Pulmonary function test/Chronic obstructive pulmonary disease分类
计算机与自动化引用本文复制引用
杜秋雨,陈楠,郭际香,章毅,刘伦旭,徐修远..基于CT图像的深度神经网络肺功能预测[J].四川大学学报（自然科学版）,2024,61(4):145-154,10.基金项目
国家自然科学基金(62106163) （62106163）
四川省自然科学基金面上项目(2023YFG0283) （2023YFG0283）
中国人工智能学会-华为MindSpore学术奖励基金(21H1235) （21H1235）
