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

Soumaya Ben Salem; Samar Zahra Ali; Anyik John Leo; Zied Lachiri; Martin Mkandawire

doi:10.3389/frai.2023.1248977

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

Front Artif Intell. 2023 Sep 13:6:1248977. doi: 10.3389/frai.2023.1248977. eCollection 2023.

Authors

Soumaya Ben Salem^{1

2}, Samar Zahra Ali², Anyik John Leo², Zied Lachiri¹, Martin Mkandawire²

Affiliations

¹ SITI Laboratory, National School of Engineers of Tunis, University of Tunis El Manar, Tunis, Tunisia.
² Department of Chemistry, School Science and Technology, Cape Breton University, Sydney, NS, Canada.

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.

Keywords: artificial intelligence; cancer differentiation; electrical impedance spectroscopy; machine learning; malignant lesion; mammography imaging.

Grants and funding

This work partially benefited from support through NSERC Discovery Grant to MM, NSERC UNSRA Scholarship to SA, CFI and Research Nova Scotia Funding, Internal Cape Breton University's RP/RISE Research Grant Programs and Mitacs Globalink Program.