了解乳腺癌检测:全面调查

4 Pub Date : 2023-12-11 DOI:10.46632/cset/1/4/1
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引用次数: 0

摘要

本文探讨了提高毫米波在乳腺癌成像中的穿透力的可能性。方法:提出了一种基于凸优化方法的场塑造技术,该技术能够提高乳房隆起分层内的场水平。结果:通过设计和模拟两个圆极化天线,对理论结果进行了数值验证。利用组织模拟模型对所设计的天线进行了实验验证,结果与理论预测十分吻合。结论证明了在有损介质中聚焦毫米波频率电磁功率的可能性。意义重大:场整形是利用毫米波检测乳腺癌的关键。索引词条--乳腺癌、微波成像、毫米波、径向线槽阵列、近场优化。提出了一种基于凸优化方法的场聚焦技术,该技术能够提高乳腺增生分层内的场水平。这里使用了一种基于凸优化的聚焦技术,以提高毫米波下乳腺癌成像场景的穿透力。通过使用聚焦孔径,乳房模型内部的场强得到了显著增强。癌症检测的关键挑战在于如何将肿瘤分为恶性和良性,而机器学习技术可以显著提高诊断的准确性。从这些方法本身来看,其设计和实施都非常复杂。数据可视化过程本身就是一个复杂的过程,如果图像有噪声,就会产生错误的结果,从而导致结果不正确。它通常包含两个主要步骤,但进一步的步骤与上述方法非常相似,这使得它变得更加复杂,因为它包括将补丁图像转换为真实图像,这使得诊断结果不准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding Breast Cancer Detection: A Comprehensive Survey
The potentialities of improving the penetration of millimeter waves for breast cancer imaging are here explored. Methods: A field shaping technique based on a convex optimization method is proposed, capable of increasing the field level inside a breast-emulating stratification. Results: The theoretical results are numerically validated via the design and simulation of two circularly polarized antennas. The experimental validation of the designed antennas, using tissue-mimicking phantoms, is provided, being in good agreement with the theoretical predictions. Conclusion: The possibility of focusing, within a lossy medium, the electromagnetic power at millimeter-wave frequencies is demonstrated. Significance: Field shaping can be a key for using millimeter waves for breast cancer detection. Index Terms—Breast Cancer, Microwave Imaging, Millimeter Waves, Radial Line Slot Arrays, Near-Field Optimization. A field focusing technique based on a convex optimization method is proposed, capable of increasing the field level inside a breast-emulating stratification. A focusing technique based on convex optimization has been used here to increase the penetration in breast cancer imaging scenarios at millimeter waves. Significant enhancements of the field level inside a breast model have been achieved by employing focused apertures. The key challenge in cancer detection is how to classify tumors into malignant or benign machine learning techniques can dramatically improves the accuracy of diagnosis. As the methods itself show that it’s much complicated in designing and implementation. The data visualization process itself is a complicated process which can generate false results if the image has noise, so the result won’t be correct. It usually contains two major steps but the further steps are much similar to the above methods ,which makes it further more complicated as it including converting patched images to real images which makes the result inaccurate in diagnosis.
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