Diagnosis of Thyroid Nodules Based on Lightweight Residual Network

Abstract

Thyroid cancer is extremely common in the population and its incidence has gradually increased in recent years. In order to help doctors diagnose thyroid nodules, many computer diagnosis algorithms have been proposed, but most of these algorithms only focus on improving the accuracy of recognition of benign and malignant thyroid nodules, the neural network model established is relatively complex, which will lead to longer inference time. In addition, due to the limitation of hardware, the model with a large number of parameters can easily make the relevant equipment unable to function normally. To solve the above problems, this paper proposes a lightweight residual network, EDSResNet, by improving ResNet-34. In the experimental part, Vgg-16, AlexNet, MobileNet_v2, ResNet, ShuffleNet_v2_x2.0, and DenseNet-121 are introduced for comparison, and the results show that the proposed EDSResNet improves 1.1% in accuracy compared with the ResNet-34, and the parameter quantity of EDSResNet is only 6.6% of that before the improvement, the number of floating point operations (Flops) is 10% of that before the improvement. Compared with another lightweight network, MobileNet_v2, EDSResNet has $8\times 10^{5}$ fewer parameters, and the accuracy, sensitivity and specificity are higher by 1.7%, 2.0% and 0.8%, respectively. After comparing with all networks, it can be found that the EDSResNet proposed in this paper has an excellent classification effect on the ultrasound image dataset of thyroid nodules while having the smallest number of parameters.

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