Version 1
: Received: 15 March 2024 / Approved: 15 March 2024 / Online: 15 March 2024 (14:42:57 CET)
How to cite:
Zha, C.; Li, L.; Zhu, F.; Zhao, Y. The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis. Preprints2024, 2024030933. https://doi.org/10.20944/preprints202403.0933.v1
Zha, C.; Li, L.; Zhu, F.; Zhao, Y. The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis. Preprints 2024, 2024030933. https://doi.org/10.20944/preprints202403.0933.v1
Zha, C.; Li, L.; Zhu, F.; Zhao, Y. The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis. Preprints2024, 2024030933. https://doi.org/10.20944/preprints202403.0933.v1
APA Style
Zha, C., Li, L., Zhu, F., & Zhao, Y. (2024). The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis. Preprints. https://doi.org/10.20944/preprints202403.0933.v1
Chicago/Turabian Style
Zha, C., Fangting Zhu and Yanzhe Zhao. 2024 "The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis" Preprints. https://doi.org/10.20944/preprints202403.0933.v1
Abstract
The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection which detects the patient's exhaled VOCs combined with sensor arrays of Convolutional Neural Network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, due to low accuracy, high complexity computation and large number of parameters make the CNN algorithms difficult transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models such as EfficientNet, LTNet has fewer parameters (32K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than that of the five existing models’, and it also offers the shortest training (844.38s and 584.67s) and inference time (23s and 14s) in the same validation set. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
