期刊
INFORMATION FUSION
卷 68, 期 -, 页码 131-148出版社
ELSEVIER
DOI: 10.1016/j.inffus.2020.11.005
关键词
Chest CT; COVID-19; Deep fusion; transfer learning; pretrained model; Discriminant correlation analysis; Micro-averaged F1
资金
- British Heart Foundation Accelerator Award, UK
- Royal Society International Exchanges Cost Share Award, UK [RP202G0230]
- Hope Foundation for Cancer Research, UK [RM60G0680]
- Medical Research Council Confidence in Concept Award, UK [MC_PC_17171]
The study aimed to develop an advanced AI system for COVID-19 classification based on chest CT images. By utilizing different algorithms and pretrained models, researchers successfully created the CCSHNet model, which can effectively detect COVID-19 and other lung infectious diseases.
Aim: : COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October 2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 million deaths. To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images. Methods: : Our dataset from local hospitals consisted of 284 COVID-19 images, 281 community-acquired pneumonia images, 293 secondary pulmonary tuberculosis images; and 306 healthy control images. We first used pretrained models (PTMs) to learn features, and proposed a novel (L, 2) transfer feature learning algorithm to extract features, with a hyperparameter of number of layers to be removed (NLR, symbolized as L). Second, we proposed a selection algorithm of pretrained network for fusion to determine the best two models characterized by PTM and NLR. Third, deep CCT fusion by discriminant correlation analysis was proposed to help fuse the two features from the two models. Micro-averaged (MA) F1 score was used as the measuring indicator. The final determined model was named CCSHNet. Results: : On the test set, CCSHNet achieved sensitivities of four classes of 95.61%, 96.25%, 98.30%, and 97.86%, respectively. The precision values of four classes were 97.32%, 96.42%, 96.99%, and 97.38%, respectively. The F1 scores of four classes were 96.46%, 96.33%, 97.64%, and 97.62%, respectively. The MA F1 score was 97.04%. In addition, CCSHNet outperformed 12 state-of-the-art COVID-19 detection methods. Conclusions: : CCSHNet is effective in detecting COVID-19 and other lung infectious diseases using first-line clinical imaging and can therefore assist radiologists in making accurate diagnoses based on CCTs.
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