Application of Deep Learning in the Prediction of Benign and Malignant Thyroid Nodules on Ultrasound Images

Abstract

In this paper, ultrasound imaging of benign and malignant thyroid nodules to predict the depth of the learning algorithm, built on circulation volume product thyroid ultrasound image neural network forecasting model. Introduced the convolutional neural network and the recurrent neural network, and combined the advantages of the convolutional neural network and the recurrent neural network, improved the prediction model, constructed the recurrent convolutional neural network prediction model and optimized the prediction model. Soc max algorithm and L2 regularization are introduced to prevent the occurrence of over-fitting. This study introduces the technology and tools required for the development of forecasting systems, the feasibility analysis of the system, demand analysis and system design and other system development preliminary work. Describes the function of the thyroid nodule prediction system and related work such as system testing. Based on the above research, thyroid ultrasound images obtained by the cooperative hospital are used as a data set, and the cyclic convolutional neural network prediction model is used to predict training and testing to the development of a thyroid nodule prediction system. The experimental results show that the prediction system has high prediction accuracy.