scholarly journals Cancer detection based on electrical impedance spectroscopy: A clinical study

2018 ◽  
Vol 9 (1) ◽  
pp. 17-23 ◽  
Author(s):  
Sepideh Mohammadi Moqadam ◽  
Parvind Kaur Grewal ◽  
Zahra Haeri ◽  
Paris Ann Ingledew ◽  
Kirpal Kohli ◽  
...  

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.

2021 ◽  
Vol 232 (2) ◽  
Author(s):  
Rakibul Islam Chowdhury ◽  
Rinku Basak ◽  
Khan Arif Wahid ◽  
Katy Nugent ◽  
Helen Baulch

2020 ◽  
Vol 28 ◽  
pp. 1679-1685
Author(s):  
Angeliki-Eirini Dimou ◽  
Ioanna Sakellariou ◽  
George M. Maistros ◽  
Nikolaos D. Alexopoulos

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1001
Author(s):  
Sooin Huh ◽  
Hye-Jin Kim ◽  
Seungah Lee ◽  
Jinwoo Cho ◽  
Aera Jang ◽  
...  

This study presents a system for assessing the freshness of meat with electrical impedance spectroscopy (EIS) in the frequency range of 125 Hz to 128 kHz combined with an image classifier for non-destructive and low-cost applications. The freshness standard is established by measuring the aerobic plate count (APC), 2-thiobarbituric acid reactive substances (TBARS), and composition analysis (crude fat, crude protein, and moisture) values of the microbiological detection to represent the correlation between EIS and meat freshness. The EIS and images of meat are combined to predict the freshness with the Adaboost classification and gradient boosting regression algorithms. As a result, when the elapsed time of beef storage for 48 h is classified into three classes, the time prediction accuracy is up to 85% compared to prediction accuracy of 56.7% when only images are used without EIS information. Significantly, the relative standard deviation (RSD) of APC and TBARS value predictions with EIS and images datum achieves 0.890 and 0.678, respectively.


Allergy ◽  
2021 ◽  
Author(s):  
Arturo O. Rinaldi ◽  
Angelica Korsfeldt ◽  
Siobhan Ward ◽  
Daniel Burla ◽  
Anita Dreher ◽  
...  

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