Classification of Deep Convolutional Neural Network in Thyroid Ultrasound Images

Abstract

Objective: To explore the application of deep convolutional neural network theory in thyroid ultrasound image system analysis and eigenvalue extraction to help medically predict the patient’s condition. Methods: The thyroid color ultrasound image dataset of our hospital was selected as the training and test samples. The comparison experiment was designed in the deep convolutional neural network learning framework to test the feasibility of the method. Results: Image information classification based on deep neural network algorithm can predict thyroid nodule lesions well, and has good accuracy in the classification test of benign and malignant nodules. Conclusion: The clinical application of deep learning method and thyroid ultrasound image feature value extraction and system analysis can improve the accuracy of clinical thyroid benign and malignant classification.