Ultrasound imaging serves as a pivotal tool for diagnosing cervical lymph node lesions. However, the diagnoses of these images largely hinge on the expertise of medical practitioners, rendering the process susceptible to misdiagnoses. Although rapidly developing deep learning has substantially improved the diagnoses of diverse ultrasound images, there remains a conspicuous research gap concerning cervical lymph nodes. The objective of our work is to accurately diagnose cervical lymph node lesions by leveraging a deep learning model. To this end, we first collected 3392 images containing normal lymph nodes, benign lymph node lesions, malignant primary lymph node lesions, and malignant metastatic lymph node lesions. Given that ultrasound images are generated by the reflection and scattering of sound waves across varied bodily tissues, we proposed the Conv-FFT Block. It integrates convolutional operations with the fast Fourier transform to more astutely model the images. Building upon this foundation, we designed a novel architecture, named US-SFNet. This architecture not only discerns variances in ultrasound images from the spatial domain but also adeptly captures microstructural alterations across various lesions in the frequency domain. To ascertain the potential of US-SFNet, we benchmarked it against 12 popular architectures through five-fold cross-validation. The results show that US-SFNet is SOTA and can achieve 92.89% accuracy, 90.46% precision, 89.95% sensitivity and 97.49% specificity, respectively.

翻译：超声成像是诊断颈部淋巴结病变的关键工具。然而，这些图像的诊断在很大程度上依赖于医疗从业者的专业知识，使得该过程容易受到误诊的影响。尽管快速发展的深度学习已显著改善了多种超声图像的诊断，但关于颈部淋巴结的研究仍存在明显空白。我们的工作旨在通过利用深度学习模型准确诊断颈部淋巴结病变。为此，我们首先收集了3392张包含正常淋巴结、良性淋巴结病变、恶性原发性淋巴结病变和恶性转移性淋巴结病变的图像。鉴于超声图像是通过声波在不同身体组织中的反射和散射生成的，我们提出了Conv-FFT模块。它将卷积操作与快速傅里叶变换相结合，以更智能地对图像进行建模。在此基础之上，我们设计了一种名为US-SFNet的新型架构。该架构不仅从空间域识别超声图像的差异，还能从频率域巧妙捕捉各类病变的微结构变化。为验证US-SFNet的潜力，我们通过五折交叉验证将其与12种流行架构进行了对比。结果表明，US-SFNet达到了最先进水平，分别实现了92.89%的准确率、90.46%的精确率、89.95%的灵敏度和97.49%的特异度。