| Literature DB >> 33584916 |
Sepideh Mohammadi Moqadam1, Parvind Kaur Grewal1, Zahra Haeri1, Paris Ann Ingledew2, Kirpal Kohli2, Farid Golnaraghi1.
Abstract
An electrical Impedance based tool is designed and developed to aid physicians performing clinical exams focusing on cancer detection. Current research envisions improvement in sensor-based measurement technology to differentiate malignant and benign lesions in human subjects. The tool differentiates malignant anomalies from nonmalignant anomalies using Electrical Impedance Spectroscopy (EIS). This method exploits cancerous tissue behavior by using EIS technique to aid early detection of cancerous tissue. The correlation between tissue electrical properties and tissue pathologies is identified by offering an analysis technique based on the Cole model. Additional classification and decision-making algorithm is further developed for cancer detection. This research suggests that the sensitivity of tumor detection will increase when supplementary information from EIS and built-in intelligence are provided to the physician.Entities:
Keywords: Cole model; Electrical Impedance (EI); LAD error function; early cancer detection; electrical Impedance spectroscopy (EIS); fitting-model
Year: 2018 PMID: 33584916 PMCID: PMC7852020 DOI: 10.2478/joeb-2018-0004
Source DB: PubMed Journal: J Electr Bioimpedance ISSN: 1891-5469
Figure 2The exploded view of the probe containing electrical impedance electrodes as well as temperature sensors.
Figure 1Skin cancer stages (source: teleskin.org)
Figure 3Design of the probe in SolidWorks and the prototype
Figure 4Use of probe and the pressure sensors over human tissue
Case studies recruited for testing the proposed method on cancerous subjects and their information
| Case Study No. | Age | Sex | Tumor Type | Tumor Position |
|---|---|---|---|---|
| Subject 1 | 90 | Female | BCC (Basal Cell Carcinoma) | Left side of nose along sidewall |
| Subject 2 | 97 | Female | SCC (Squamous Cell Carcinoma) | Left cheek center |
| Subject 3 | 81 | Male | SCC (Squamous Cell Carcinoma) | Right Temple of head |
| Subject 4 | 93 | Female | SCC (Squamous Cell Carcinoma) | Left cheek |
| Subject 5 | 87 | Female | BCC (Basal Cell Carcinoma) | Left cheek under the eye |
| Subject 6 | 93 | Female | BCC (Basal Cell Carcinoma) | Left mid neck |
| Subject 7 | 87 | Male | BCC (Basal Cell Carcinoma) | Right cheek |
| Subject 8 | 92 | Male | SCC (Squamous Cell Carcinoma) | Forearm |
| Subject 9 | 66 | Female | BCC (Basal Cell Carcinoma) | Upper left cheek |
| Subject 10 | 65 | Male | BCC (Basal Cell Carcinoma) | Left nasal wing |
Figure 5The Debye case of the Cole model circuit (α=1).
Figure 6Imaginary part of admittance versus its real part (Cole plot).
Figure 7Paths of high and low frequency currents in a biological tissue.
Figure 8Admittance plot of the tumorous subjects and the contralateral healthy part.
Figure 9Comparison of the performance of 3 classification methods on the data.
The performance of various classification methods on raw and processed data.
| Sensitivity % | Specificity % | Accuracy % | Resubstitution % | Cross Validation Error % | ||
|---|---|---|---|---|---|---|
| NB Gaussian | 7.14 | 50 | 31.25 | 68.75 | 54.17 | |
| NB Kernel | 12.5 | 12.5 | 12.5 | 87.5 | 87.5 | |
| NB Gaussian | 38.46 | 73.68 | 59.38 | 40.63 | 31.2 | |
| NB Kernel | 44.44 | 73.91 | 65.63 | 34.38 | 33.3 | |
| SVM | 20 | 58.82 | 40.63 | 59.38 | 53.12 | |
| NB Gaussian | 66.67 | 90 | 81.25 | 18.75 | 16.67 | |
| NB Kernel | 70 | 86.36 | 81.25 | 18.75 | 13.12 | |
| SVM | 50 | 80 | 68.75 | 31.25 | 18.75 | |