Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images
| Autor: | Tianjiao Liu, Chao Sun, Qing Chang, Qianqian Guo, Weidong Sun, Jinpeng Yao, Shao-Hang Zhang, Lijuan Niu, Yu-Kang Zhang, Xi Wang |
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| Rok vydání: | 2020 |
| Předmět: |
Thyroid nodules
medicine.medical_specialty Local binary patterns CAD Sensitivity and Specificity 030218 nuclear medicine & medical imaging 03 medical and health sciences Deep Learning 0302 clinical medicine medicine Humans Diagnosis Computer-Assisted Thyroid Nodule Ultrasonography Receiver operating characteristic Computers business.industry Deep learning Ultrasound General Medicine medicine.disease Feature (computer vision) Computer-aided diagnosis 030220 oncology & carcinogenesis Radiology Artificial intelligence business |
| Zdroj: | Medical Physics. 47:3952-3960 |
| ISSN: | 2473-4209 0094-2405 |
| DOI: | 10.1002/mp.14301 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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