Genetic Optimization of Morphological Filters with Applications in Breast Cancer Detection

2004 ◽  
pp. 250-259 ◽  
Author(s):  
Lucia Ballerini ◽  
Lennart Franzén
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Genetic Optimization ◽  
Morphological Filters

A 7.21-bit ENOB 100MS/s 1.51mW Inverter based Pipelined ADC with Digital Calibration for Breast Cancer Detection System

2020 ◽  
Vol 140 (6) ◽  
pp. 585-591
Author(s):  
Yoshihiro Masui ◽  
Tsukasa Nishimiya ◽  
Allex Uemi ◽  
Akihiro Toya ◽  
Takamaro Kikkawa
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Detection System ◽  
Breast Cancer Detection ◽  
Digital Calibration ◽  
Pipelined Adc

An Information-theoretical Model for Breast Cancer Detection

2008 ◽  
Vol 47 (04) ◽  
pp. 322-327 ◽  
Author(s):  
D. Blokh ◽  
N. Zurgil ◽  
I. Stambler ◽  
E. Afrimzon ◽  
Y. Shafran ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Hamming Distance ◽  
Formal Model ◽  
Methodological Approach ◽  
Breast Cancer Detection ◽  
Receptor Expression ◽  
Diagnostic Model ◽  
Model Parameters ◽  
Two Stages

Summary Objectives: Formal diagnostic modeling is an important line of modern biological and medical research. The construction of a formal diagnostic model consists of two stages: first, the estimation of correlation between model parameters and the disease under consideration; and second, the construction of a diagnostic decision rule using these correlation estimates. A serious drawback of current diagnostic models is the absence of a unified mathematical methodological approach to implementing these two stages. The absence of aunified approach makesthe theoretical/biomedical substantiation of diagnostic rules difficult and reduces the efficacyofactual diagnostic model application. Methods: The present study constructs a formal model for breast cancer detection. The diagnostic model is based on information theory. Normalized mutual information is chosen as the measure of relevance between parameters and the patterns studied. The “nearest neighbor” rule is utilized for diagnosis, while the distance between elements is the weighted Hamming distance. The model concomitantly employs cellular fluorescence polarization as the quantitative input parameter and cell receptor expression as qualitative parameters. Results: Twenty-four healthy individuals and 34 patients (not including the subjects analyzed for the model construction) were tested by the model. Twenty-three healthy subjects and 34 patients were correctly diagnosed. Conclusions: The proposed diagnostic model is an open one,i.e.it can accommodate new additional parameters, which may increase its effectiveness.

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