Abstract：Objective To investigate the value of U-Det model in differentiating benign from malignant pulmonary nodules on CT
images. Methods Samples of 150 patients with small pulmonary nodules who underwent pathological examination in our hospital
from June 2021 to January 2022 were selected. Malignant samples (n=104) and non-malignant samples (n=46) were retained and
increased to 800 samples each. According to the ratio of 7∶3, the samples of training set (n=1120) and verification set (n=480)
were randomly divided into two groups. According to the training samples, the pre-trained convolutional neural network architecture
ResNet50 was trained, and the convolutional neural network computer aided system was established to test the ability of screening
malignant lesions of small pulmonary nodules. Meanwhile, 1400 pathological images of LUNA16 were selected as the test set to
test the diagnostic value of U-Det model. Results The average loss rate of training samples in U-Det model was 0.126%±0.046%,
and the average loss rate of verification samples was 0.135%±0.053%. The average accuracy of training samples and verification
samples in U-Det model was 88.42%±4.21% and 89.01%±4.09% respectively. The receiver operating characteristic curve showed
that the prediction accuracy of U-Det, U-Net and ResNet50 models decreased successively (P<0.05). The diagnostic accuracy,
sensitivity, specificity, positive and negative predictive values of U-Det model in LUNA16 test set were the highest, followed by U-Net
and ResNet50. Conclusion U-Det model has a high value in the differential diagnosis of benign from malignant pulmonary nodules
on CT images. It can be used in the diagnosis of benign from malignant small pulmonary nodules.
张靖. 基于U-Det模型对肺内小结节CT图像
良恶性鉴别的价值分析[J]. 中国医疗设备, 2023, 38(9): 36-40.
ZHANG Jing. Analysis of the Value of U-Det Model in Differentiating Benign from Malignant Small
Pulmonary Nodules on CT Images. China Medical Devices, 2023, 38(9): 36-40.
