Single-Photon Emission Computed Tomography Image-Assisted Diagnosis of Thyroid Diseases under Convolutional Network Neural Algorithm

Abstract

The aim of this study was to investigate the value of single-photon emission computed tomography (SPECT) based on the convolutional neural network (CNN) algorithm in thyroid diseases. Thirty-five patients with thyroid disease from the hospital were selected as the observation group, and another 35 healthy volunteers were selected as the control group. The constructed model of SPECT based on the CNN algorithm was compared with the backpropagation neural network (BPNN) algorithm, which was then applied to the SPECT of 35 patients with thyroid disease. It turned out that as the number of iterations increased, the parameter training of CNN was gradually sufficient, the network model was continuously optimized, and the accuracy gradually increased. From the data results, the Dice value of the proposed CNN algorithm was higher than that of the BPNN algorithm and the segmentation effect was relatively good. The visual index of the thyroid/neck of the observation group (2.68 ± 1.32) was remarkably inferior to that of the control group (12.347.54) ( $P<0.05$ ). The visual index of the thyroid/submandibular gland in the observation group (1.02 ± 0.41) was remarkably inferior to that of the control group (8.89 ± 4.86) ( $P<0.05$ ). The visual index of the thyroid/parotid gland in the observation group (1.04 ± 0.58) was remarkably inferior to that of the control group (8.53 ± 4.25) ( $P<0.05$ ). In addition, 99mTcO4-SPECT had a sensitivity of 95.2%, a specificity of 90.3%, and an accuracy of 91.5% in the diagnosis of thyroid diseases. The area under the curve of the receiver operating characteristic curve for 99mTcO4-SPECT diagnosis of thyroid disease is 0.958, and the 95% confidence interval is 0.834∼1. In summary, the SPECT based on the CNN algorithm proposed in this study has a good segmentation effect and can accurately locate the anatomical information of thyroid diseases, which can replace the traditional diagnostic methods for the diagnosis of thyroid diseases.