基于卷积神经网络的甲状腺影像诊断研究

Research on Thyroid Imaging Diagnosis Based on Convolution Neural Network

随着卷积神经网络在医疗图像领域的成功应用,进一步推动了医学影像设备性能的提升。通过利用卷积神经网络方法,对甲状腺影像进行系统的分析,有效预测患者的病情发展态势,切实保障诊断效率的提高,整体看具有较为显著的临床应用价值。鉴于此,该文通过分析卷积神经网络的基本结构、工作机制、基本特点,探讨基于卷积神经网络的甲状腺影像识别方法,并为甲状腺影像诊断提出几点思考,以期有效推动卷积神经网络在甲状腺影像诊断中的应用改革进程。

With the successful application of convolutional neural network in the field of medical imaging,the performance of medical imaging equipment is further improved.Through the use of convolution neural network method,systematic analysis of thyroid imaging can effectively predict the development trend of the patient's condition and effectively ensure the improvement of diagnosis efficiency,which has a more significant clinical application value.In view of this,this paper analyzes the basic structure,working mechanism and basic characteristics of convolutional neural network,discusses the thyroid image recognition method based on convolutional neural network,and puts forward some thoughts for thyroid imaging diagnosis in order to effectively promote the application and reform process of convolutional neural network in thyroid imaging diagnosis.