| Title: | A Novel Framework with High Diagnostic Sensitivity for Lung Cancer Detection by Electronic Nose |
| Author(s): | Lu B; Fu L; Nie B; Peng Z; Liu H; |
| Address: | "Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400030, China. Key Laboratory of Biotechnology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400030, China" |
| ISSN/ISBN: | 1424-8220 (Electronic) 1424-8220 (Linking) |
| Abstract: | "The electronic nose (e-nose) system is a newly developing detection technology for its advantages of non-invasiveness, simple operation, and low cost. However, lung cancer screening through e-nose requires effective pattern recognition frameworks. Existing frameworks rely heavily on hand-crafted features and have relatively low diagnostic sensitivity. To handle these problems, gated recurrent unit based autoencoder (GRU-AE) is adopted to automatically extract features from temporal and high-dimensional e-nose data. Moreover, we propose a novel margin and sensitivity based ordering ensemble pruning (MSEP) model for effective classification. The proposed heuristic model aims to reduce missed diagnosis rate of lung cancer patients while maintaining a high rate of overall identification. In the experiments, five state-of-the-art classification models and two popular dimensionality reduction methods were involved for comparison to demonstrate the validity of the proposed GRU-AE-MSEP framework, through 214 collected breath samples measured by e-nose. Experimental results indicated that the proposed intelligent framework achieved high sensitivity of 94.22%, accuracy of 93.55%, and specificity of 92.80%, thereby providing a new practical means for wide disease screening by e-nose in medical scenarios" |
| Keywords: | "Aged Algorithms Breath Tests/methods Case-Control Studies Diagnosis, Computer-Assisted/*methods Early Detection of Cancer *Electronic Nose Female Humans Lung Neoplasms/*diagnosis Male Middle Aged Missed Diagnosis Models, Statistical *Pattern Recognition, ;" |
| Notes: | "MedlineLu, Binchun Fu, Lidan Nie, Bo Peng, Zhiyun Liu, Hongying eng 81671850/National Natural Science Foundation of China/ Multicenter Study Switzerland 2019/12/11 Sensors (Basel). 2019 Dec 3; 19(23):5333. doi: 10.3390/s19235333" |
