18 June 2020>: Clinical Research
Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images
Xi Wei 1ACDEG* , Ming Gao 2BEF , Ruiguo Yu 3BCD , Zhiqiang Liu 3BCE , Qing Gu 4BC , Xun Liu 5BC , Zhiming Zheng 6BC , Xiangqian Zheng 2BC , Jialin Zhu 1ABCDEF* , Sheng Zhang 1BDDOI: 10.12659/MSM.926096
Med Sci Monit 2020; 26:e926096
Figure 1 Pathways of experiments. Our experimental pathways mainly included three parts. (A) Data desensitization, removal of the sections of the patient’s personal information in the images. (B) Training and validation of ensemble learning for classification of thyroid nodules. In the segmentation part, the nodule area was manually marked and used to train the segmentation model. ROI and mask were extracted by the segmentation model. Then, three weak models were trained and combined to obtain an advanced classification model. (C) Comparison experiments with radiologists and other deep learning models, and external validation experiment. We then compared performance of the classification model with that of three ultrasound radiologists and four state-of-the-art deep learning models. Finally, we conducted an external validation using an independent dataset.