Artificial neural networks and statistical classification applied to Electrical Impedance Spectroscopy data for Melanoma diagnosis in Dermatology (DermaSense)

2018 ◽  
Author(s):  
S. Gilou ◽  
C. Dimitrousis ◽  
A. Zogkas ◽  
C. Kemanetzi ◽  
C. Korfitis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Impedance Spectroscopy ◽  
Electrical Impedance ◽  
Electrical Impedance Spectroscopy ◽  
Spectroscopy Data ◽  
Statistical Classification ◽  
Melanoma Diagnosis ◽  
Artificial Neural

scholarly journals Dimensionality reduction methods for Impedance Spectroscopy data of biological materials

2021 ◽  
Vol 2008 (1) ◽  
pp. 012009
Author(s):  
R Cavalieri ◽  
P Bertemes-Filho
Keyword(s):  
Neural Networks ◽  
Impedance Spectroscopy ◽  
Dimensionality Reduction ◽  
Electrical Impedance ◽  
Biological Materials ◽  
Electrical Impedance Spectroscopy ◽  
Spectroscopy Data ◽  
Data Set ◽  
Reduction Methods ◽  
Pca Method

Abstract Electrical impedance spectroscopy combined with Neural Networks can be a powerful combination to identify biological materials. This paper utilizes a data set containing two biological samples taken from different species and applies the most popular methods of dimensionality reduction. This is done in order to find out which method is able to minimize computational demand and maximize accuracy in the classification test. This paper proposes that the classic PCA method is the fastest and the most accurate under the configurations used.

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