Electric Impedance Imaging of the Mammary Gland in Circumstances of Skin Abnormality or Damage

Korotkova, M; Karpov, A; Machín, Mercedes Ferrer; Tsofin, Yu; Tsyplyonkov, V.; Tchayev, A.

doi:10.1007/978-3-642-03879-2_80

Cited by 1 publication

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“…Such applications have been used as an aid tool for specialists for detecting different illnesses [42], [43]. CAD systems have been mainly exploited for detecting breast cancer in X-Ray mammograms [44]. As already mentioned, the main aim of this paper is to apply machine learning techniques over a dataset comprising recognized risk factors as well as breast-conductivity indexes obtained from the use of EIM as a potential risk factor.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning for the Analysis of Conductivity From Mono Frequency Electrical Impedance Mammography as a Breast Cancer Risk Factor

et al. 2021

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Computational approaches have been used for analyzing risk factors together with conventional mammograms for breast cancer detection. Currently, other screening methods like electro-impedance mammography are available. Notwithstanding, as far as we know there is not related work evaluating the role of electrical-conductivity index of the mammary gland as a quantitative factor for early detection of breast cancer. This paper aims to demonstrate the importance of including breast conductivity index as a quantitative local risk-factor by analyzing a dataset of Mexican patients from a machine learning perspective. There are 12 attributes distributed into two groups: electrical-conductivity (3) and medical records (9). According to the obtained results with unsupervised methods, the performance in terms of accuracy of using only electrical-conductivity (43%) is better than using all available features (38%) and the medical records (33%). On the other hand, we identified that SVM achieves higher results in comparison with other algorithms when only the electrical-features are used. The obtained results demonstrate the important role of conductivity index as a quantitative local risk-factor for being considered in screening processes. Besides, it emerges as an important aspect to be included in the development of automatic tools for experts to perform breast cancer diagnosis.INDEX TERMS Electro-impedance, conductivity, machine learning, mammography MEIK, risk factor.

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Section: Methodsmentioning

confidence: 99%