| Title | Evaluation of a deep learning-based computer-aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images |
| Authors | Sun, Chao Zhan, Yukang Chang, Qing Liu, Tianjiao Zhang, Shaohang Wang, Xi Guo, Qianqian Yao, Jinpeng Sun, Weidong Niu, Lijuan |
| Affiliation | Chinese Acad Med Sci & Peking Union Med Coll, Canc Hosp, Natl Clin Res Ctr Canc, Dept Ultrasound,Natl Canc Ctr, Beijing 100021, Peoples R China Capital Med Univ, Beijing Tiantan Hosp, Dept Ultrasound, Beijing 100050, Peoples R China Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China Peking Univ, Beijing Haidian Hosp, Dept Ultrasound, Haidian Sect,Hosp 3, Beijing 100080, Peoples R China |
| Keywords | CLASSIFICATION VARIABILITY TEXTURE |
| Issue Date | Jun-2020 |
| Publisher | MEDICAL PHYSICS |
| Abstract | Purpose Computer-aided diagnosis (CAD) systems assist in solving subjective diagnosis problems that typically rely on personal experience. A CAD system has been developed to differentiate malignant thyroid nodules from benign thyroid nodules in ultrasound images based on deep learning methods. The diagnostic performance was compared between the CAD system and the experienced attending radiologists. Methods The ultrasound image dataset for training the CAD system included 651 malignant nodules and 386 benign nodules while the database for testing included 422 malignant nodules and 128 benign nodules. All the nodules were confirmed by pathology results. In the proposed CAD system, a support vector machine (SVM) is used for classification and fused features which combined the deep features extracted by a convolutional neural network (CNN) with the hand-crafted features such as the histogram of oriented gradient (HOG), local binary patterns (LBP), and scale invariant feature transform (SIFT) were obtained. The optimal feature subset was formed by selecting these fused features based on the maximum class separation distance and used as the training sample for the SVM. Results The accuracy, sensitivity, and specificity of the CAD system were 92.5%, 96.4%, and 83.1%, respectively, which were higher than those of the experienced attending radiologists. The areas under the ROC curves of the CAD system and the attending radiologists were 0.881 and 0.819, respectively. Conclusions The CAD system for thyroid nodules exhibited a better diagnostic performance than experienced attending radiologists. The CAD system could be a reliable supplementary tool to diagnose thyroid nodules using ultrasonography. Macroscopic features in ultrasound images, such as the margins and shape of thyroid nodules, could influence the diagnostic efficiency of the CAD system. |
| URI | http://hdl.handle.net/20.500.11897/607132 |
| ISSN | 0094-2405 |
| DOI | 10.1002/mp.14301 |
| Indexed | SCI(E) |
| Appears in Collections: | 第三医院 |
