Fuzzy Ordination of Breast Tissue with Electrical Impedance Spectroscopy Measurements

Yuvalı, Meliz; Kavalcıoğlu, Cemal; Kaba, Şerife; Işın, Ali

doi:10.1007/978-3-030-35249-3_19

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…However, we can only measure these features using electrical impedance spectroscopy. We aimed to calculate six impedance features that can evaluate the capacitive characteristics of breast cancer tissues [37]. These six impedance features are highly important clinically to the diagnosis of breast cancer in the early stages [38].…”

Section: Resultsmentioning

confidence: 99%

Estimation of Impedance Features and Classification of Carcinoma Breast Cancer Using Optimization Techniques

Asadi

2023

BioMedInformatics

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Breast cancer is the most prevalent form of cancer and the primary cause of cancer-related mortality among women globally. Breast cancer diagnosis involves multiple variables, making it a complex process. Therefore, the accurate estimation of features for diagnosing breast cancer is of great importance. The present study used a dataset of 21 patients with carcinoma breast cancer. Polynomial regression analysis was used to non-invasively estimate six impedance features for the diagnosis of breast cancer, including the phase angle at 500 KHz (PA500), impedance distance between spectral ends (DA), area normalized by DA (A/DA), maximum of the spectrum (Max IP), the distance between impedivity (ohm) at zero frequency and the real part of the maximum frequency point (DR), and length of the spectral curve (P). The results indicated that the polynomial degrees needed to estimate the PA500, DA, A/DA, Max IP, DR, and P features based on tumor size were 2, 2, 3, 3, 2, and 2, respectively. Additionally, we utilized a nonlinear constrained optimization (NCO) analysis to calculate the eight threshold levels for the classification of the impedance features. The deduction of eight classifications for each feature may also be an effective tool for decision-making in breast cancer. These findings may help oncologists to estimate the impedance features for breast cancer diagnosis non-invasively.

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Section: Resultsmentioning

confidence: 99%

Estimation of Impedance Features and Classification of Carcinoma Breast Cancer Using Optimization Techniques

Asadi

2023

BioMedInformatics

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Breast cancer classification application based on QGA-SVM

Dong¹,

Chen

et al. 2023

IFS

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Early diagnosis of breast cancer plays an important role in improving survival rate. Physiological changes of breast tissue can be observed and measured through medical electrical impedance, and the results can be used as a preliminary diagnosis by doctors before treatment. In this paper, quantum genetic algorithm (QGA) and support vector machine (SVM) were combined to classify breast tissues to help clinicians in diagnosis. The algorithm uses QGA to optimize the parameters of SVM and improve the classification performance of SVM. In this experiment, the electrical impedance data measured from breast tissue provided by UCI [58] was used as the data set. Objectively speaking, the data volume of the data set is small and the representativeness is not strong enough. However, the experimental results show that QGA-SVM shows better classification performance, and it is better than SVM.

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Fuzzy Ordination of Breast Tissue with Electrical Impedance Spectroscopy Measurements

Cited by 2 publications

References 8 publications

Estimation of Impedance Features and Classification of Carcinoma Breast Cancer Using Optimization Techniques

Estimation of Impedance Features and Classification of Carcinoma Breast Cancer Using Optimization Techniques

Breast cancer classification application based on QGA-SVM

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