Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
Department of Electrical Engineering, Taif University, 26571, Taif, Saudi Arabia.
Sci Rep. 2023 Aug 15;13(1):13253. doi: 10.1038/s41598-023-40494-x.
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.
本文提出了一种通过数值模拟验证的新型女性乳房异常组织检测技术。该技术与 X 射线乳房摄影术非常相似；然而，它不是使用 X 射线获取乳房图像，而是利用低频电磁场。为了获取乳房印记，使用了超表面，它可以被视为类似于 X 射线胶片。为了在乳房组织内实现深且充分的穿透，激励源是工作在 200MHz 的简单窄带偶极天线。超表面被设计为在相同频率下工作。检测机制基于使用机器学习技术将从受检乳房获得的印记与参考情况（健康乳房）进行比较。使用该系统，不仅可以检测肿瘤（良性或恶性），还可以确定肿瘤的位置和大小。值得注意的是，发现深度学习模型实现了非常高的分类准确性。
