Breast Tissue Classification based on Electrical Impedance Spectroscopy

Liu, Chang; Chang, Tiantian; Li, Changxing

doi:10.2991/itms-15.2015.56

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Several studies have previously used EIS and machine learning approaches to classify different tissue types in vitro, for instance, breast tissue [14], lung tissue [15], and prostate cancer tissue [16]. To the best of authors' knowledge, no studies have focused on the use of EIS and machine learning to characterize and classify adipose tissues.…”

Section: Introductionmentioning

confidence: 99%

Adipose Tissue Characterization With Electrical Impedance Spectroscopy and Machine Learning

Dapsance,

Hou,

Dufour

et al. 2023

IEEE Sens. Lett.

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Biological tissues have variable passive electrical properties depending on their cellular constitution. Electrical impedance spectroscopy (EIS) is commonly used to monitor cell and tissue characteristics. By measuring the impedance of a sample at various frequencies, it is possible to collect information regarding cell size and shape, cell membrane properties, or cytoplasm conductivity. From the perspective of longitudinal structural monitoring, bioimpedance measurements outrank traditional tissue analysis methods, such as fixation and slicing, owing to their nondestructive nature. Machine learning can be used to automatically process the impedance data and make real-time classifications of tissue types. Here, we present preliminary results on ex-vivo mouse adipose tissue measurements using EIS and further data processing and classification using machine learning models.

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Section: Introductionmentioning

confidence: 99%

Adipose Tissue Characterization With Electrical Impedance Spectroscopy and Machine Learning

Dapsance,

Hou,

Dufour

et al. 2023

IEEE Sens. Lett.

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“…Using these methods, authors have achieved accuracies ranging from 76% to 83%. Liu et al used the SVM algorithm for the classification of the UCI breast tissue data set and an average accuracy of 80% was obtained [19]. In the study of Ayyappan, breast tissue classification was carried out with different machine learning classifiers [20].…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Networks Model for Breast Tissue Classification

Biçer

Yanık

2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Diagnosis of breast cancer and the determination of cancer type are essential information for cancer research in monitoring and managing the disease. Artificial intelligence techniques developed in recent years have led to many developments in medicine, as any information about the patient has become more valuable. Especially, artificial intelligence methods used in the detection and classification of cancer tissues directly assist physicians and contribute to the management of the treatment process. This study aims to classify breast tissues with ten different tissue characteristics utilizing the breast tissue data set, which has 106 electrical impedance spectroscopies taken from 64 patients in the UCI Machine Learning Repository database. Various machine learning algorithms such as k-nearest neighbors, support vector machine, decision tree, self-organizing fuzzy logic, and convolutional neural networks are used to classify these tissues with the accuracy of 81%, 78%, 82%, 92%, and 96%, respectively. This study demonstrated the benefit of the usage of convolutional neural networks in cancer detection and tissue classification. Compared to traditional methods, convolutional neural networks provided a more reliable and better results.

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