Pulmonary nodule detection based on improved ResNet-50 and its cloud system development
To reduce the misdiagnosis or missed diagnosis caused by subjective judgment when doctors analyze lung CT im-ages,the application effects of four convolutional neural network models,namely ResNet-18,ResNet-34,ResNet-50,and VGG16,in lung image classification tasks were compared.Ultimately,ResNet-50 was determined as the base training model,and transfer learning was used to optimize this model to further enhance its performance.The experimental results show that the improved ResNet-50 model significantly increases the accuracy of pulmonary nodule detection and classification,reaching 89.55%,which is significantly superior to 52.27%achieved by VGG16.Additionally,a cloud-based pulmonary image visualization diagnostic system was developed using Streamlit.Users can upload lung CT images through the interface,and the system will automatically detect pul-monary nodules and output classification results.This system is easy to operate and assists doctors in making quick diagnoses and patients in timely detection of their conditions.
transfer learningResNet-50Pulmonary nodule detectionLung CT imagesStreamlitcloud system
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