Establishment of a chest-CT-image-based classification deep learning model for pneumonia prediction - ZENG Meng; ZHAO Na; WANG Xianqi (陆军军医大学学报, 2023)
Objective To establish an AI model based on chest CT images to achieve rapid classification prediction for bacterial, fungal, and viral (including COVID-19) infectious pneumonia. Methods Chest CT imaging data of 559 bacterial, fungal and non-COVID-19 viral pneumonia patients admitted to the First Affiliated Hospital of Army Medical University from 2013 to 2020 and 53 COVID-19 patients to the Chongqing University Three Gorges Hospital from January 2020 to December 2020 were collected and analyzed retrospectively.Firstly, 4 typical deep neural networks (Resnet_18, Efficientnet_b5, ViT, and Swin-Transformer) were used to construct image-level triple and quadruple classification prediction models, and the optimal model was selected by validation in an independent test set.Then, the effects of single-image and three-fused-images to construct dataset on the models were analyzed.Finally, voting by image category and random forest were carried out to make the patient-level classification prediction, respectively.The precision, recall rate, specificity, F1 value, AUC, and accuracy were employed to evaluate the performance of the models in order to screen out the best performing AI prediction model. Results The Swin Transformer model performed best in image-level classification, with a triple classification accuracy of 0.932 and a quadruple classification accuracy of 0.948.After the model was constructed with single-image and three-fused-images, a model, named as Swin-Transformer_C, which was further improved with fused images, showed good performance, with a triple classification accuracy and AUC of 0.931 and 0.989, and with a quadruple classification accuracy and AUC of 0.952 and 0.990, respectively, in the test set.Patient-level categorization using the Swin-transformer_C model integrated with random forest was more effective, with a triple classification accuracy and AUC of 0.984 and 0.987, and with a quadruple classification accuracy and AUC of 0.967 and 0.971, respectively.Three other networks, Resnet_18, Efficientent_b5 and Vit, also achieved good results, but the overall effectiveness was lower than that of the Swin-transformer network. Conclusion The deep learning model Swin-transformer_C, built on fused dataset, shows best performance in image-level classification when compared to the other 4 models, and it achieves optimal performance by integrating with random forest classifier in patient-level classification.Our study demonstrates that deep learning can be used for rapid classification prediction of pneumonia types due to different pathogen infections.