Early breast cancer detection and differentiation tool based on tissue impedance characteristics and machine learning

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.