US-SFNet: A Spatial-Frequency Domain-based Multi-branch Network for Cervical Lymph Node Lesions Diagnoses in Ultrasound Images

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.