Background:
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.
Objective:
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.
Methods:
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.
Results:
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.
Conclusion:
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.
Thyroid Nodules Classification in Medical Ultrasound Images using Deep Learning
