基于CNN迁移学习的甲状腺结节检测方法

将人工智能应用到医学图像中可减少医生工作量和患者的重复检查。针对现有甲状腺结节检测方法处理过程繁琐、特征提取困难等问题，提出一种基于卷积神经网络（CNN）的甲状腺结节检测方法。针对数据样本量小的限制，提出利用预训练与迁移学习改善网络性能的策略。根据不同结构CNN能够提取不同层次特征的特点，提出融合浅层与深层网络的方法。通过医院收集的3 414张图片对提出的方法进行验证，最终准确率为91.60%，灵敏度为90.08%，特异性为93.24%，接收者操作特征曲线下面积为96.55%。

Thyroid nodule detection method based on CNN and transfer learning

The application of artificial intelligence to medical images can reduce the workload of doctors and the repeated examination of patients. Aiming at the problems of the existing thyroid nodule detection methods such as complex processing procedures and difficult feature extraction, a thyroid nodule detection method based on Convolutional Neural Network（CNN） is put forward. For the small data sample size restrictions, the strategy for improving network performance using pre-training and transfer learning is proposed. According to the trait that different structure CNN can extract different levels of features, a method to simultaneously fuse two networks to improve the accuracy is advanced. The proposed method is validated on 3414 images collected from hospital with the accuracy of 91.60%, sensitivity of 90.08%, specificity of 93.24% and AUC of 96.55%.