School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China.
School of Automation, Harbin University of Science and Technology, Harbin 150080, China.
Comput Intell Neurosci. 2019 Jan 15;2019:6212759. doi: 10.1155/2019/6212759. eCollection 2019.
Thyroid disease has now become the second largest disease in the endocrine field; SPECT imaging is particularly important for the clinical diagnosis of thyroid diseases. However, there is little research on the application of SPECT images in the computer-aided diagnosis of thyroid diseases based on machine learning methods. A convolutional neural network with optimization-based computer-aided diagnosis of thyroid diseases using SPECT images is developed. Three categories of diseases are considered, and they are Graves' disease, Hashimoto disease, and subacute thyroiditis. A modified DenseNet architecture of convolutional neural network is employed, and the training method is improved. The architecture is modified by adding the trainable weight parameters to each skip connection in DenseNet. And the training method is improved by optimizing the learning rate with flower pollination algorithm for network training. Experimental results demonstrate that the proposed method of convolutional neural network is efficient for the diagnosis of thyroid diseases with SPECT images, and it has superior performance than other CNN methods.
甲状腺疾病现已成为内分泌领域的第二大疾病；SPECT 成像对于甲状腺疾病的临床诊断尤为重要。然而，基于机器学习方法的 SPECT 图像在甲状腺疾病的计算机辅助诊断中的应用研究较少。利用 SPECT 图像，开发了一种基于优化的甲状腺疾病卷积神经网络计算机辅助诊断方法。考虑了 Graves 病、桥本甲状腺炎和亚急性甲状腺炎三种疾病。采用了改进的卷积神经网络密集连接网络结构，并对训练方法进行了改进。通过在密集连接网络中的每个跳跃连接中添加可训练权重参数来修改网络结构，并通过使用花授粉算法优化学习率来改进网络训练方法。实验结果表明，基于卷积神经网络的方法对于 SPECT 图像诊断甲状腺疾病是有效的，并且比其他 CNN 方法具有更好的性能。
