Ultrasonography is widely used in the clinical diagnosis of thyroid nodules. Ultrasound images of thyroid nodules have different appearances, interior features, and blurred borders that are difficult for a physician to diagnose into malignant or benign types merely through visual recognition. The development of artificial intelligence, especially deep learning, has led to great advances in the field of medical image diagnosis. However, there are some challenges to achieve precision and efficiency in the recognition of thyroid nodules. In this work, we propose a deep learning architecture, you only look once v3 dense multireceptive fields convolutional neural network (YOLOv3-DMRF), based on YOLOv3. It comprises a DMRF-CNN and multiscale detection layers. In DMRF-CNN, we integrate dilated convolution with different dilation rates to continue passing the edge and the texture features to deeper layers. Two different scale detection layers are deployed to recognize the different sizes of the thyroid nodules. We used two datasets to train and evaluate the YOLOv3-DMRF during the experiments. One dataset includes 699 original ultrasound images of thyroid nodules collected from a local health physical center. We obtained 10,485 images after data augmentation. Another dataset is an open-access dataset that includes ultrasound images of 111 malignant and 41 benign thyroid nodules. Average precision (AP) and mean average precision (mAP) are used as the metrics for quantitative and qualitative evaluations. We compared the proposed YOLOv3-DMRF with some state-of-the-art deep learning networks. The experimental results show that YOLOv3-DMRF outperforms others on mAP and detection time on both the datasets. Specifically, the values of mAP and detection time were 90.05 and 95.23% and 3.7 and 2.2 s, respectively, on the two test datasets. Experimental results demonstrate that the proposed YOLOv3-DMRF is efficient for detection and recognition of thyroid nodules for ultrasound images.

中文翻译：

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用于甲状腺结节检测和识别的高效深度学习架构。

超声检查广泛用于甲状腺结节的临床诊断。甲状腺结节的超声图像具有不同的外观，内部特征和模糊的边界，这使得医师仅凭视觉识别就难以诊断出其为恶性或良性类型。人工智能（尤其是深度学习）的发展已导致医学图像诊断领域的巨大进步。然而，在识别甲状腺结节方面要达到精确性和效率方面存在一些挑战。在这项工作中，我们提出了一种深度学习架构，您只需基于YOLOv3一次查看v3密集多接收域卷积神经网络（YOLOv3-DMRF）。它包括DMRF-CNN和多尺度检测层。在DMRF-CNN中，我们将具有不同膨胀率的膨胀卷积集成在一起，以继续将边缘和纹理特征传递到更深的层。部署了两个不同的尺度检测层，以识别甲状腺结节的不同大小。在实验过程中，我们使用了两个数据集来训练和评估YOLOv3-DMRF。一个数据集包括从当地卫生物理中心收集的699个甲状腺结节的原始超声图像。数据扩充后，我们获得了10,485张图像。另一个数据集是开放式数据集，其中包括111个恶性和41个甲状腺良性结节的超声图像。平均精度（AP）和平均平均精度（mAP）用作定量和定性评估的指标。我们将拟议的YOLOv3-DMRF与一些最新的深度学习网络进行了比较。实验结果表明，YOLOv3-DMRF在两个数据集上的mAP和检测时间均优于其他方法。具体来说，在两个测试数据集上，mAP值和检测时间分别为90.05％和95.23％以及3.7和2.2 s。实验结果表明，所提出的YOLOv3-DMRF能有效地检测和识别超声图像中的甲状腺结节。