A deep learning‐based method for detecting and classifying the ultrasound images of suspicious thyroid nodules. Issue 12 (17th November 2021)

• Record Type: Journal Article
• Authors: Zhao, Zijian; Yang, Congmin; Wang, Qian; Zhang, Huawei; Shi, Linlin; Zhang, Zhiwen
• Is Part Of: Medical physics. Volume 48:Issue 12(2021)
• Page Start: 7959
• Page End: 7970
• Publication Date: 2021-11-17
• Subjects: computer‐aided diagnosis -- deep learning -- thyroid nodule -- ultrasound; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15319
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: The incidence of thyroid cancer has significantly increased in the last few decades. However, diagnosis of the thyroid nodules is labor and time intensive for radiologists and strongly depends on the personal experience of the radiologists. In this pursuit, the present study envisaged to develop a deep learning‐based computer‐aided diagnosis (CAD) method that enabled the automatic detection and classification of suspicious thyroid nodules in order to reduce the unnecessary fine‐needle aspiration biopsy. Methods: The CAD method consisted of two main parts: detecting the location of thyroid nodules using a multiscale detection network and classifying the detected thyroid nodules by an attention‐based classification network. Results: The performance of the proposed method was evaluated and compared with that of other state‐of‐the‐art deep learning methods and experienced radiologists. The proposed detection method outperformed three other detection architectures (average precision, 82.1% vs. 78.3%, 77.2%, and 74.8%). Moreover, the classification method showed a superior performance compared with four other state‐of‐the‐art classification networks (accuracy, 94.8% vs. 91.2%, 85.0%, 80.8%, and 72.1%) and that by experienced radiologists (mean value of area under the curve, 0.941 vs. 0.833). Conclusions: Our study verified the high efficiency of the proposed detection method. The findings can help improve the diagnostic performance of radiologists. However, the developed CAD system requires more training and evaluation in a large‐population study.