Department of Ultrasound, Weihai Maternal and Child Health Hospital, Weihai, China.
Department of Equipment, Weihai Maternal and Child Health Hospital, Weihai, China.
Curr Med Imaging Rev. 2020;16(2):174-180. doi: 10.2174/1573405615666191023104751.
Thyroid nodules are a common clinical entity with high incidence. Ultrasound is often employed to detect and evaluate thyroid nodules. The development of an efficient automated method to detect thyroid nodules using ultrasound has the potential to reduce both physician workload and operator-dependence.
To study the method of automatic detection of thyroid nodules based on deep learning using ultrasound, and to obtain the detection method with higher accuracy and better performance.
A total of 1200 ultrasound images of thyroid nodules and 800 ultrasound thyroid images without nodule are collected. An improved faster R-CNN based detection method of thyroid nodule is proposed. Instead of using VGG16 as the backbone, ResNet is employed as the backbone for faster R-CNN. SVM, CNN and Faster-RCNN methods are used for thyroid nodule detection test. Precision, sensitivity, specificity and F1-score indicators are used to evaluate the detection performance of different methods.
The method based on deep learning is superior to that based on SVM. Faster R-CNN method and the improved method are better than CNN method. Compared with VGG16 as the backbone, RestNet101 backbone based faster R-CNN method achieves better thyroid detection effect. From the accuracy index, the proposed method is 0.084, 0.032 and 0.019 higher than SVM, CNN and faster R-CNN, respectively. Similar results can be seen in precision, sensitivity, specificity and F1-Score indicators.
The proposed method of deep learning achieves the best performance values with the highest true positive and true negative detection compared to other methods and performs best in the detection of thyroid nodules.
甲状腺结节是一种常见的临床实体，发病率很高。超声常用于检测和评估甲状腺结节。开发一种高效的自动方法，利用超声检测甲状腺结节，具有降低医生工作量和操作人员依赖性的潜力。
研究基于深度学习的甲状腺结节自动检测方法，获得准确性更高、性能更好的检测方法。
收集了 1200 张甲状腺结节超声图像和 800 张无结节甲状腺超声图像。提出了一种基于改进的 Faster R-CNN 的甲状腺结节检测方法。该方法以 ResNet 代替 VGG16 作为 Faster R-CNN 的主干网络，采用 SVM、CNN 和 Faster-RCNN 方法对甲状腺结节进行检测试验。使用精度、敏感度、特异性和 F1 分数指标来评估不同方法的检测性能。
基于深度学习的方法优于基于 SVM 的方法。Faster R-CNN 方法和改进的方法优于 CNN 方法。与 VGG16 作为主干网络相比，基于 ResNet101 主干网络的 Faster R-CNN 方法实现了更好的甲状腺检测效果。从准确性指标来看，所提出的方法分别比 SVM、CNN 和 Faster R-CNN 高 0.084、0.032 和 0.019。在精度、敏感度、特异性和 F1-Score 指标上也可以看到类似的结果。
与其他方法相比，深度学习提出的方法具有最佳的性能值，具有最高的真阳性和真阴性检测率，在甲状腺结节检测中表现最佳。
