Mammography using low-frequency electromagnetic fields with deep learning.

Autor: Akbari-Chelaresi H; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada., Alsaedi D; Department of Electrical Engineering, Taif University, 26571, Taif, Saudi Arabia., Mirjahanmardi SH; Department of Medical Physics, Stanford University, Stanford, CA, 94304, USA., El Badawe M; Soundskrit Inc., 1751 Rue Richardson, No. 5102, Montreal, Canada., Albishi AM; Electrical Engineering Department, King Saud University, 11421, Riyadh, Saudi Arabia., Nayyeri V; School of Advanced Technologies, Iran University of Science and Technology, Tehran, 16846-13114, Iran. nayyeri@iust.ac.ir., Ramahi OM; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada. oramahi@uwaterloo.ca.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Aug 15; Vol. 13 (1), pp. 13253. Date of Electronic Publication: 2023 Aug 15.
DOI: 10.1038/s41598-023-40494-x
Abstrakt: In this paper, a novel technique for detecting female breast anomalous tissues is presented and validated through numerical simulations. The technique, to a high degree, resembles X-ray mammography; however, instead of using X-rays for obtaining images of the breast, low-frequency electromagnetic fields are leveraged. To capture breast impressions, a metasurface, which can be thought of as analogous to X-rays film, has been employed. To achieve deep and sufficient penetration within the breast tissues, the source of excitation is a simple narrow-band dipole antenna operating at 200 MHz. The metasurface is designed to operate at the same frequency. The detection mechanism is based on comparing the impressions obtained from the breast under examination to the reference case (healthy breasts) using machine learning techniques. Using this system, not only would it be possible to detect tumors (benign or malignant), but one can also determine the location and size of the tumors. Remarkably, deep learning models were found to achieve very high classification accuracy.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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