Olfactory Diagnosis Model for Lung Health Evaluation Based on Pyramid Pooling and SHAP-Based Dual Encoders,ACS Sensors

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Olfactory Diagnosis Model for Lung Health Evaluation Based on Pyramid Pooling and SHAP-Based Dual Encoders
ACS Sensors ( IF 8.2 ) Pub Date : 2024-09-09 , DOI: 10.1021/acssensors.4c01584
Jingyi Peng ₁ , Haixia Mei ₁ , Ruiming Yang ₁ , Keyu Meng ₁ , Lijuan Shi ₁ , Jian Zhao ₁ , Bowei Zhang ₂ , Fuzhen Xuan ₂ , Tao Wang ₂ , Tong Zhang ₃

Affiliation

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.

中文翻译：

基于金字塔池和SHAP双编码器的肺部健康评估嗅觉诊断模型

这项研究引入了一种利用呼出气体评估肺部健康的新型深度学习框架。该框架协同集成了金字塔池和双编码器网络，利用 SHapley Additive exPlanations (SHAP) 导出的特征重要性来增强其预测能力。该框架专门设计用于有效区分吸烟者、慢性阻塞性肺疾病 (COPD) 患者和对照受试者。金字塔池化结构通过池化四个尺度的特征来聚合多级全局信息。 SHAP 评估八个传感器的特征重要性。两种编码器架构根据重要性处理不同的特征集，从而优化性能。此外，利用滑动窗口技术和对原始数据的白噪声增强增强了模型的鲁棒性。在5倍交叉验证中，该模型的平均准确率达到96.40%，比单编码器金字塔池化模型高出10.77%。进一步优化 Transformer 卷积层中的滤波器和金字塔模块中的池化大小，将准确率提高到 98.46%。这项研究提供了一种有效的工具来识别吸烟和慢性阻塞性肺病的影响，以及一种利用深度学习技术解决复杂生物医学问题的新方法。

更新日期：2024-09-09

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