Author(s)

Limin Xia, Shanglin Lei, Huiyao Chen, Haoyang Wang

Corresponding Author

Limin Xia

ABSTRACT

Deep learning technology assisted ultrasound image diagnosis can improve the accuracy and efficiency of detection. In this paper we propose an improved U-Net convolution network for ultrasound image segmentation. The network replaces the noise excitation function NHReLU and NHSeLU with the noise excitation function ReLU, and adds weight parameters to the cost function. By predicting on two scales, it handles the problem of the size change of the marked area in the ultrasound image well, and improves the segmentation effect of the lymph node ultrasound image. Use networks such as VGG, ResNet, and DenseNet to predict the benign and malignant areas of lymph node lesions. Experiments show that the segmentation network has excellent performance, its Dice coefficient reaches 0.90, and the model can well prevent overfitting. In addition, the prediction of benign and malignant indicators under a small sample has been improved, which provides a new method for the application of deep learning technology in ultrasound image detection.

KEYWORDS

Deep learning, noise excitation function, medical image