{'fraction_non_alphanumeric': 0.020626432391138275, 'fraction_numerical': 0.012987012987012988, 'mean_word_length': 5.179245283018868, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A computed tomography image recognition method of pulmonary nodules

Pulmonary nodules are a type of lung disease that can involve multiple organs in severe cases, so early diagnosis and treatment are particularly important for patients. This paper introduces an automatic classification model of pulmonary nodule CT images based on transfer learning. Firstly, data preprocessing is performed on the CT images of pulmonary nodules, including geometric transformation of the data set to achieve data enhancement and improve the generalization ability of the model. Xception is selected as the benchmark network of the classification model, and the pre-trained weights of the model on the ImageNet dataset are migrated as the initial weights of the new model. SGDM, RMSProp and Adam optimization algorithms are used to optimize respectively, and the newly constructed model is fine-tuned and trained with the preprocessed data set to realize the secondary classification of CT images of pulmonary nodules. The experimental results show that the Xception + SGDM optimization algorithm model based on transfer learning has a recognition accuracy rate of 97.86%, an accuracy rate of 98.65%, a recall rate of 96.10%, and an F1 score of 97.37%, all of which are superior to existing automatic identification methods.