Detection of Thyroid Nodules with Ultrasound Images Based on Deep Learning

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Author(s): Xia Yu*, Hongjie Wang, Liyong Ma.

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

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Abstract:

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 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.

Method: 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 highest true positive and true negative detection compared to other methods and performs best in the detection of thyroid nodules.

Keywords: Thyroid nodule, classification, ultrasound image, deep learning, convolutional neural network (CNN), faster R-CNN

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1573405615666191023104751
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