Yuxiang Wang
ABSTRACT
Thyroid nodule is a common clinical disease, and its incidence is increasing year by year. With the continuous development of medical imaging technology, most thyroid nodules can be detected by ultrasound images, but it is difficult for even the most experienced radiologists to classify the nature of thyroid nodules accurately every time. The reason for this is the uneven appearance of benign and malignant nodules. Therefore, this paper proposes an algorithm for the classification of benign and malignant thyroid nodules based on ConvNeXt. The algorithm contains the following main contents. The first is to remove artificial markers from the ultrasound image. Then, data expansion was performed on the dataset of thyroid ultrasound images. Finally, an attention mechanism is introduced into the ConvNeXt convolutional neural network, and the enhanced ultrasound image set is used as a training set to perform transfer learning on the pre-trained ConvNeXt convolutional neural network to classify benign and malignant nodules. The method proposed in this paper achieved a correct rate of 99.32%, a sensitivity of 99.82%, and a specificity of 96.13% on the public dataset of thyroid nodules.The final experiment shows that artificial markers removal and data expansion of the original image can improve the training effect. The image region concerned by the network model is drawn by Grad-CAM. It can be concluded that the introduction of attention mechanism into ConvNeXt network model can make the model pay more attention to the texture information of nodules in ultrasound images and describe the difference between the properties of focal and non-focal areas.
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- Benign and Malignant Classification of Thyroid Nodules Based on ConvNeXt
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