1. Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images.
- Author
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Wei X, Gao M, Yu R, Liu Z, Gu Q, Liu X, Zheng Z, Zheng X, Zhu J, and Zhang S
- Subjects
- Adenocarcinoma, Follicular classification, Adenocarcinoma, Follicular diagnostic imaging, Adenoma classification, Adolescent, Adult, Aged, Aged, 80 and over, Carcinoma, Neuroendocrine classification, Carcinoma, Neuroendocrine diagnostic imaging, Female, Goiter, Nodular classification, Granuloma diagnostic imaging, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Thyroid Cancer, Papillary classification, Thyroid Carcinoma, Anaplastic classification, Thyroid Carcinoma, Anaplastic diagnostic imaging, Thyroid Neoplasms classification, Thyroid Nodule classification, Ultrasonography, Young Adult, Adenoma diagnostic imaging, Deep Learning, Goiter, Nodular diagnostic imaging, Thyroid Cancer, Papillary diagnostic imaging, Thyroid Neoplasms diagnostic imaging, Thyroid Nodule diagnostic imaging
- Abstract
BACKGROUND Thyroid nodules are extremely common and typically diagnosed with ultrasound whether benign or malignant. Imaging diagnosis assisted by Artificial Intelligence has attracted much attention in recent years. The aim of our study was to build an ensemble deep learning classification model to accurately differentiate benign and malignant thyroid nodules. MATERIAL AND METHODS Based on current advanced methods of image segmentation and classification algorithms, we proposed an ensemble deep learning classification model for thyroid nodules (EDLC-TN) after precise localization. We compared diagnostic performance with four other state-of-the-art deep learning algorithms and three ultrasound radiologists according to ACR TI-RADS criteria. Finally, we demonstrated the general applicability of EDLC-TN for diagnosing thyroid cancer using ultrasound images from multi medical centers. RESULTS The method proposed in this paper has been trained and tested on a thyroid ultrasound image dataset containing 26 541 images and the accuracy of this method could reach 98.51%. EDLC-TN demonstrated the highest value for area under the curve, sensitivity, specificity, and accuracy among five state-of-the-art algorithms. Combining EDLC-TN with models and radiologists could improve diagnostic accuracy. EDLC-TN achieved excellent diagnostic performance when applied to ultrasound images from another independent hospital. CONCLUSIONS Based on ensemble deep learning, the proposed approach in this paper is superior to other similar existing methods of thyroid classification, as well as ultrasound radiologists. Moreover, our network represents a generalized platform that potentially can be applied to medical images from multiple medical centers.
- Published
- 2020
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