Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors
Abstract
:1. Introduction
- Support vector machine (SVM);
- k-nearest neighbors algorithm (k-NN);
- Artificial neural network (ANN).
2. Materials and Methods
- A half-filled sample bag is connected to the inlet valve. The bag is loaded with the same weight for all measurements. At this moment, the inlet (2) and outlet (3) valves of the sampling chamber are closed.
- In the interface of the data collection program, a button is pressed and the reading and transmission of data at a frequency of 30 Hz to a personal computer begins.
- At the moment of transition from the heating phase to the cooling phase of the sensors (Figure 3—mark 5000 ms), valves (2) and (3) open for exactly one second. The same opening time is used for valves (2) and (3), and the weight of the load on the bag provides the same volume of gas sample (~ 250 mL) introduced into the 1 L sampling chamber.
- After the valves are closed, data collection continues with the sample gas inside the chamber. The process of collecting data continues until the time stamp of 90,000 ms. The total residence time of the sample in the chamber does not exceed 90 s.
- After 90 s, the sample chamber purge is automatically turned on for 120 s. The instrument is then ready to analyze the next sample. These time intervals were selected experimentally and take into account the inertia of the sensors.
3. Results
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Stage of the Pathological Process (According to TNM System) | Number of Patients with Malignant Pathology by Tumor Location | |||||
---|---|---|---|---|---|---|
Morphological Diagnosis Confirmation Method | ||||||
Lungs | Larynx | Oral Cavity | Oropharynx | Tongue | The Mucous Membrane of the Alveolar Process of the Lower Jaw | |
Stage T1 | 2 | 1 | ||||
Stage T2 | 3 | 2 | 3 | 2 | 4 | 1 |
Stage T3 | 9 | 1 | 2 | |||
Stage T4 | 4 | 1 | 1 |
№ | Sensor | Sensitivity |
---|---|---|
1 | MP503 | Alcohol, Smoke, Isobutane, Methanol |
2 | WSP2110 | Toluene, Benzene, Methane |
3 | MQ3 | Alcohol |
4 | MQ2 | Isobutane, Propane, Methane, Alcohol, Hydrogen, Smoke |
5 | MQ7 | CO |
6 | MQ131 | O3 |
7 | MQ135 | NH3,NOx, Alcohol, Benzene, Smoke, CO2 |
8 | MQ8 | Hydrogen |
9 | MQ138 | n-Hexane, Benzene, NH3, Alcohol, Smoke, CO |
10 | TGS822 | Methane, CO, Isobutane, n-Hexane, Benzene, Ethanol, Acetone |
11 | TGS2602 | Ethanol, Toluene, NH3, H2S |
12 | TGS2620 | Methane, CO, Isobutane, Hydrogen, Ethanol |
13 | TGS2600 | Isobutane, Hydrogen, Ethanol |
14 | TGS2603 | Hydrogen, H2S, Ethanol, Methanethiol, Trimethylamine etc. |
Parameter | Value |
---|---|
Accuracy | 81.8% |
Sensitivity | 90.7% |
Specificity | 61.4% |
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Chernov, V.I.; Choynzonov, E.L.; Kulbakin, D.E.; Obkhodskaya, E.V.; Obkhodskiy, A.V.; Popov, A.S.; Sachkov, V.I.; Sachkova, A.S. Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors. Diagnostics 2020, 10, 677. https://doi.org/10.3390/diagnostics10090677
Chernov VI, Choynzonov EL, Kulbakin DE, Obkhodskaya EV, Obkhodskiy AV, Popov AS, Sachkov VI, Sachkova AS. Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors. Diagnostics. 2020; 10(9):677. https://doi.org/10.3390/diagnostics10090677
Chicago/Turabian StyleChernov, Vladimir I., Evgeniy L. Choynzonov, Denis E. Kulbakin, Elena V. Obkhodskaya, Artem V. Obkhodskiy, Aleksandr S. Popov, Victor I. Sachkov, and Anna S. Sachkova. 2020. "Cancer Diagnosis by Neural Network Analysis of Data from Semiconductor Sensors" Diagnostics 10, no. 9: 677. https://doi.org/10.3390/diagnostics10090677