  Booming deep learning has substantially improved the diagnosis for diverse
lesions in ultrasound images, but a conspicuous research gap concerning
cervical lymph node lesions still remains. The objective of this work is to
diagnose cervical lymph node lesions in ultrasound images by leveraging a deep
learning model. To this end, we first collected 3392 cervical ultrasound 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 SFUSNet. SFUSNet not only discerns variances in ultrasound images from
the spatial domain but also adeptly captures micro-structural alterations
across various lesions in the frequency domain. To ascertain the potential of
SFUSNet, we benchmarked it against 12 popular architectures through five-fold
cross-validation. The results show that SFUSNet is the state-of-the-art model
and can achieve 92.89% accuracy. Moreover, its average precision, average
sensitivity and average specificity for four types of lesions achieve 90.46%,
89.95% and 97.49%, respectively.
