卷积神经网络
分级(工程)
接收机工作特性
超声波
人工智能
计算机科学
模式识别(心理学)
放射科
医学
机器学习
工程类
土木工程
作者
Juntao Shao,Jingjing Zheng,Bing Zhang
出处
期刊:Journal of the Acoustical Society of America
[Acoustical Society of America]
日期:2020-09-01
卷期号:148 (3): 1529-1535
被引量:10
摘要
The performances of deep convolutional neural network (DCNN) modeling and transfer learning (TF) for thyroid tumor grading using ultrasound imaging were evaluated. This retrospective study included input patient data (ultrasound B-mode image sets) assigned to the training group (115 participants) or testing group (28 participants). DCNN (ResNet50) and TF (ResNet50, ResNet101, ResNet152, VGG16, Inception V3, and DenseNet201), which trains a convolutional neural network that has been pre-trained on ImageNet, were used for image classification based on thyroid tumor grade. Supervised training was performed by using the DCNN or TF model to minimize the difference between the output data and clinical grading. The performances of the DCNN and TF models were assessed in the testing dataset with receiver operating characteristic analyses. Results showed that TF based on Resnet50 and VGG16 had better performance than DCNN (ResNet50) in differentiating thyroid tumor with areas under the receiver operating characteristic (AUCs) curve more than 0.8. However, TF based on ResNet101, ResNet152, InceptionV3, and Densenet201 had equal or worse performances than DCNN (ResNet50) in grading thyroid tumor with AUCs less than 0.5. TF based on ResNet50 and VGG16 had a superior performance compared to DCNN (ResNet50) model for grading thyroid tumors based on ultrasound images.
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