Ultrasonography AKA diagnostic sonography is a noninvasive imaging technique that allows the analysis of an organic structure, thanks to the ultrasonic waves. It is a valuable diagnosis method and is also seen as the evidence‐based diagnostic method for thyroid nodules. The diagnosis, however, is visually made by the practitioner. The automatic discrimination of benign and malignant nodules would be very useful to report Thyroid Imaging Reporting. In this paper, we propose a fine‐tuning approach based on deep learning using a Convolutional Neural Network model named resNet‐50. This approach allows improving the effectiveness of the classification of thyroid nodules in ultrasound images. Experiments have been conducted on 814 ultrasound images and the results show that our proposed approach dramatically improves the accuracy of the classification of thyroid nodules and outperforms The VGG‐19 model.

中文翻译：

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使用微调深度卷积神经网络对超声图像中的甲状腺结节进行分类和诊断

超声波检查又名诊断超声波检查是一种无创成像技术，由于超声波，它允许分析有机结构。它是一种有价值的诊断方法，也被视为甲状腺结节的循证诊断方法。然而，诊断是由从业者在视觉上做出的。良恶性结节的自动区分对于报告甲状腺成像报告非常有用。在本文中，我们提出了一种基于深度学习的微调方法，使用名为 resNet-50 的卷积神经网络模型。这种方法允许提高超声图像中甲状腺结节分类的有效性。