Wang Lulu
Biomedical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China.
Micromachines (Basel). 2023 Jul 21;14(7):1462. doi: 10.3390/mi14071462.
Medical imaging techniques, including X-ray mammography, ultrasound, and magnetic resonance imaging, play a crucial role in the timely identification and monitoring of breast cancer. However, these conventional imaging modalities have their limitations, and there is a need for a more accurate and sensitive alternative. Microwave imaging has emerged as a promising technique for breast cancer detection due to its non-ionizing, non-invasive, and cost-effective nature. Recent advancements in microwave imaging and sensing techniques have opened up new possibilities for the early diagnosis and treatment of breast cancer. By combining microwave sensing with machine learning techniques, microwave imaging approaches can rapidly and affordably identify and classify breast tumors. This manuscript provides a comprehensive overview of the latest developments in microwave imaging and sensing techniques for the early detection of breast cancer. It discusses the principles and applications of microwave imaging and highlights its advantages over conventional imaging modalities. The manuscript also delves into integrating machine learning algorithms to enhance the accuracy and efficiency of microwave imaging in breast cancer detection.
医学成像技术，包括X线乳腺摄影、超声和磁共振成像，在乳腺癌的及时识别和监测中发挥着关键作用。然而，这些传统成像方式存在局限性，因此需要一种更准确、更灵敏的替代方法。微波成像因其非电离、非侵入性和成本效益高的特点，已成为一种有前景的乳腺癌检测技术。微波成像和传感技术的最新进展为乳腺癌的早期诊断和治疗开辟了新的可能性。通过将微波传感与机器学习技术相结合，微波成像方法可以快速且经济地识别和分类乳腺肿瘤。本文全面概述了用于早期乳腺癌检测的微波成像和传感技术的最新进展。它讨论了微波成像的原理和应用，并强调了其相对于传统成像方式的优势。本文还深入探讨了集成机器学习算法以提高微波成像在乳腺癌检测中的准确性和效率。
