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Open AccessArticle

A Novel Framework with High Diagnostic Sensitivity for Lung Cancer Detection by Electronic Nose

1
Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400030, China
2
Key Laboratory of Biotechnology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
3
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400030, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5333; https://doi.org/10.3390/s19235333
Received: 7 November 2019 / Revised: 27 November 2019 / Accepted: 29 November 2019 / Published: 3 December 2019
(This article belongs to the Special Issue Electronic Noses)
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. View Full-Text
Keywords: lung cancer; autoencoder; ensemble pruning; electronic nose; volatile organic compounds lung cancer; autoencoder; ensemble pruning; electronic nose; volatile organic compounds
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Lu, B.; Fu, L.; Nie, B.; Peng, Z.; Liu, H. A Novel Framework with High Diagnostic Sensitivity for Lung Cancer Detection by Electronic Nose. Sensors 2019, 19, 5333.

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