Abstract：
:With the development of deep learning, its application in diagnosis of benign and malignant thyroid nodules has been widely concerned. However, it is difficult to obtain medical images, resulting in insufficient number of data, which contradicts the large amount of data required for acquiring effective deep learning diagnostic models. A multi-view ensemble learning based on voting mechanism is proposed herein to boost the performance of the models trained by small-dataset thyroid nodule ultrasound images. The method integrates three kinds of diagnosis results which are obtained from 3-view dataset which is composed of thyroid nodule ultrasound images, medical features extracted based on U-Net output and useful features selected by mRMR from the statistical features and texture features. To obtain preliminary diagnosis results, the images are utilized for training GoogleNet. For improving the results, supplementary methods were proposed based on the medical features and the selected features. To analyze the contribution of these features and acquire two groups of diagnosis results, the designed Xgboost classifier is utilized for obtaining two groups of features respectively. Subsequently, the boosting final results are obtained through majority voting mechanism. Furthermore, the proposed method is utilized to diagnose sequence images (the images extracted by frame from videos) to solve the poor results caused by slight differences. Finally, better final results are obtained for both of the normal dataset and the sequence dataset (consisting of sequence images). Compared with the accuracies obtained by only training deep learning models with small datasets, the diagnostic accuracies of the above two datasets are improved to 92.11% and 92.54% respectively by utilizing the proposed method.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Chen Y,Li D,Zhang X,Jin J,Shen Ydoi
10.1016/j.media.2020.101819subject
Has Abstractpub_date
2021-01-01 00:00:00pages
101819eissn
1361-8415issn
1361-8423pii
S1361-8415(20)30183-3journal_volume
67pub_type
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