Abstract

To study the automatic detection method of hyperthyroidism based on the deep learning of the modified LeNet-5 network and to establish a detection method with higher precision and better performance, a total of 180 facial images of patients with hyperthyroidism in the ultrasound imaging department of the HwaMei hospital were collected as well as 180 facial images of healthy persons were collected to serve as the control group. A method was proposed for the detection of hyperthyroidism based on the modified LeNet-5. Three test groups were designed by randomized controlled experiment design for the detection of hyperthyroidism, in which the control group was tested manually by experienced doctors, the LeNet-5 network learning group adopted the classical LeNet-5 network learning algorithm and the experimental group used the modified LeNet-5 network learning algorithm. Evaluation indices included the accuracy, detection efficiency, sensitivity, specificity and F1 score of the detection. At the same time, differences between the two algorithms in the detection of thyroid nodules were compared. There was no significant difference between the 3 groups in the accuracy and specificity of detection of hyperthyroidism. In terms of the detection efficiency and sensitivity, the performance of the network learning algorithm group and learning group was better than that of the control group. Both the network learning algorithm group and experimental group could detect the thyroid nodules accurately, but there was no significant difference in the accuracy of detecting the type of thyroid nodules. The correct rate of malignant thyroid nodules was significantly higher than that of the benign thyroid nodules. The modified LeNet-5 network algorithm showed acceptable consistency with experienced doctors in the detection of hyperthyroidism, and this method was useful for the exclusion of thyroid malignant tumours and can be used as a simple method to exclude and identify malignant thyroid tumours.