ArticleFrontiers in artificial intelligence2023
Early breast cancer detection and differentiation tool based on tissue impedance characteristics and machine learning.
Article in Frontiers in artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 10 citations in OpenAlex.
- Classification of lung cancer tissue using bioimpedance spectroscopy and fractional-order circuit modeling.Physical and engineering sciences in medicine · 2026Article
- Deep learning-driven multi-omics analysis: enhancing cancer diagnostics and therapeutics.Briefings in bioinformatics · 2025Review
- Explainable AI-enabled hybrid deep learning architecture for breast cancer detection.Frontiers in immunology · 2025Article
- Effects of temperature on electrical impedance of biological tissues: ex-vivo measurements.Journal of electrical bioimpedance · 2024Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During Basic screening, it is challenging, if not impossible to detect breast cancer especially in the earliest stage of tumor development. However, measuring the electrical impedance of biological tissue can detect abnormalities even before being palpable. Thus, we used impedance characteristics data of various breast tissue to develop a breast cancer screening tool guided and augmented by a deep learning (DL). A DL algorithm was trained to ideally classify six classes of breast cancer based on electrical impedance characteristics data of the breast tissue. The tool correctly predicted breast cancer in data of patients whose breast tissue impedance was reported to have been measured when other methods detected no anomaly in the tissue. Furthermore, a DL-based approach using Long Short-Term Memory (LSTM) effectively classified breast tissue with an accuracy of 96.67%. Thus, the DL algorithm and method we developed accurately augmented breast tissue classification using electrical impedance and enhanced the ability to detect and differentiate cancerous tissue in very early stages. However, more data and pre-clinical is required to improve the accuracy of this early breast cancer detection and differentiation tool.
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