School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China.
Human Resources Department, The Third Affiliated Hospital OF C.C.U.C.M, Changchun, 130117, China.
BMC Med Imaging. 2024 Nov 18;24(1):314. doi: 10.1186/s12880-024-01486-z.
In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyroid nodules due to its high real-time capabilities and low invasiveness. However, thyroid images obtained from current ultrasound tests often have low resolution and are plagued by significant noise interference. Regional differences in medical conditions and varying levels of physician experience can impact the accuracy and efficiency of diagnostic results. With the advancement of deep learning technology, deep learning models are used to identify whether a nodule in a thyroid ultrasound image is benign or malignant. This helps to close the gap between doctors' experience and equipment differences, improving the accuracy of the initial diagnosis of thyroid nodules. To cope with the problem that thyroid ultrasound images contain complex background and noise as well as poorly defined local features. in this paper, we first construct an improved ResNet50 classification model that uses a two-branch input and incorporates a global attention lightening module. This model is used to improve the accuracy of benign and malignant nodule classification in thyroid ultrasound images and to reduce the computational effort due to the two-branch structure.We constructed a U-net segmentation model incorporating our proposed ACR module, which uses hollow convolution with different dilation rates to capture multi-scale contextual information for feature extraction of nodules in thyroid ultrasound images and uses the results of the segmentation task as an auxiliary branch for the classification task to guide the classification model to focus on the lesion region more efficiently in the case of weak local features. The classification model is guided to focus on the lesion region more efficiently, and the classification and segmentation sub-networks are respectively improved specifically for this study, which is used to improve the accuracy of classifying the benign and malignant nature of the nodules in thyroid ultrasound images. The experimental results show that the four evaluation metrics of accuracy, precision, recall, and f1 of the improved model are 96.01%, 93.3%, 98.8%, and 96.0%, respectively. The improvements were 5.7%, 1.6%, 13.1%, and 7.4%, respectively, compared with the baseline classification model.
近年来，结节性甲状腺疾病的发病率逐年上升。由于超声检查具有实时性好、无创伤等优点，已成为甲状腺结节的常规诊断工具。但目前的超声检查得到的甲状腺图像分辨率往往较低，且受到较大的噪声干扰。医疗条件的地区差异和医生经验水平的不同，都会影响诊断结果的准确性和效率。随着深度学习技术的发展，深度学习模型被用于识别甲状腺超声图像中的结节是良性还是恶性。这有助于缩小医生经验和设备差异造成的差距，提高甲状腺结节的初步诊断准确性。为了解决甲状腺超声图像中包含复杂背景和噪声以及局部特征不明确的问题，我们首先构建了一个改进的 ResNet50 分类模型，该模型采用双分支输入，并结合全局注意力轻量级模块。该模型用于提高甲状腺超声图像中良恶性结节分类的准确性，并由于采用双分支结构，减少计算量。我们构建了一个结合了我们提出的 ACR 模块的 U-net 分割模型，该模块使用具有不同扩张率的空心卷积来捕获甲状腺超声图像中结节的多尺度上下文信息，并将分割任务的结果作为辅助分支用于分类任务，以引导分类模型在局部特征较弱的情况下更有效地关注病变区域。引导分类模型更有效地关注病变区域，并分别针对此研究改进分类和分割子网络，用于提高甲状腺超声图像中结节良恶性分类的准确性。实验结果表明，改进模型的准确率、精确率、召回率和 f1 四个评估指标分别为 96.01%、93.3%、98.8%和 96.0%，与基线分类模型相比，分别提高了 5.7%、1.6%、13.1%和 7.4%。
