文献类型：期刊文献

英文题名：Olfactory Diagnosis Model for Lung Health Evaluation Based on Pyramid Pooling and SHAP-Based Dual Encoders

作者：Peng, Jingyi[1];Mei, Haixia[1];Yang, Ruiming[1];Meng, Keyu[1];Shi, Lijuan[1];Zhao, Jian[1];Zhang, Bowei[2];Xuan, Fuzhen[2];Wang, Tao[2];Zhang, Tong[3]

机构：[1]Changchun Univ, Key Lab Intelligent Rehabil & Barrier Free Disable, Minist Educ, Changchun 130022, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[3]Jilin Univ, Coll Elect Sci & Engn, State Key Lab Integrated Optoelect, Changchun 130012, Peoples R China

年份：2024

卷号：9

期号：9

起止页码：4934

外文期刊名：ACS SENSORS

收录：;EI(收录号：20243817043407);WOS:【SCI-EXPANDED(收录号:WOS:001309502500001)】；

基金：This work was supported by the National Natural Science Foundation of China (52205586, 62371299, 62301314, and 62020106006) and the China Postdoctoral Science Foundation (2023M732198).

语种：英文

外文关键词：pyramid pooling; hierarchical encoding; modelinterpretability; electronic nose; diagnosis model

摘要：This study introduces a novel deep learning framework for lung health evaluation using exhaled gas. The framework synergistically integrates pyramid pooling and a dual-encoder network, leveraging SHapley Additive exPlanations (SHAP) derived feature importance to enhance its predictive capability. The framework is specifically designed to effectively distinguish between smokers, individuals with chronic obstructive pulmonary disease (COPD), and control subjects. The pyramid pooling structure aggregates multilevel global information by pooling features at four scales. SHAP assesses feature importance from the eight sensors. Two encoder architectures handle different feature sets based on their importance, optimizing performance. Besides, the model's robustness is enhanced using the sliding window technique and white noise augmentation on the original data. In 5-fold cross-validation, the model achieved an average accuracy of 96.40%, surpassing that of a single encoder pyramid pooling model by 10.77%. Further optimization of filters in the transformer convolutional layer and pooling size in the pyramid module increased the accuracy to 98.46%. This study offers an efficient tool for identifying the effects of smoking and COPD, as well as a novel approach to utilizing deep learning technology to address complex biomedical issues.