ABSTRACT
Objective To develop a diagnostic model based on deep neural network for intelligent discrimination of thyroid function. Methods A total of 1616 patients ( 283 males, 1333 females, average age:52 years) who underwent thyroid imaging between May 2016 and June 2018 were selected. According to the clinical diagnosis, the 1616 cases included 299 normal thyroid cases, 876 hyperthyroidism cases and 441 hypothyroidism cases. Feature extraction and learning training were performed on 1000 training set sam-ples by two deep neural network models ( AlexNet;deep convolution generative adversarial networks ( DCGAN) ) using deep learning algorithm. Performance verifications were implemented on 616 test set samples. The con-sistency between the verification results of the two models and the clinical diagnosis was analyzed by Kappa test. Meanwhile, the time advantage of the intelligent diagnosis models was analyzed. Results The average diagnostic time of AlexNet model was 1 s/case, and the classification accuracy for normal thyroid, hyperthy-roidism, hypothyroidism were 82.29％(79/96), 94.62％(369/390), 100％(130/130), respectively. The Kappa value between results of AlexNet model and clinical diagnosis was 0.886 ( P<0.05) . The average di-agnostic time of DCGAN model was 1 s/case, and the classification accuracy for normal thyroid, hyperthy-roidism, hypothyroidism were 85.42％(82/96), 95.64％(373/390), 99.23％(129/130), respectively. The Kappa value between results of DCGAN model and clinical diagnosis was 0.904 ( P<0.05) . Conclusion The deep neural network intelligent diagnosis model can quickly determine the functional status of thyroid gland in thyroid imaging, and it has a high recognition accuracy, thus providing a new method for thyroid image review.
ABSTRACT
Y0, then the diagnosis of hypertension with liver cirrhosis obtained. The positive rate of diagnosis is 95% and the specificity is 96% and 91% respectively, much better than those in type B ultrasonography or gastric endoscopy, 78% or 75% respectively (P