Research on the classification of CT images of COVID-19 by pre-training model based on self-attention mechanism
Proceedings of SPIE - The International Society for Optical Engineering
; 12602, 2023.
Artigo
em Inglês
| Scopus | ID: covidwho-20245409
ABSTRACT
Nowadays, with the outbreak of COVID-19, the prevention and treatment of COVID-19 has gradually become the focus of social disease prevention, and most patients are also more concerned about the symptoms. COVID-19 has symptoms similar to the common cold, and it cannot be diagnosed based on the symptoms shown by the patient, so it is necessary to observe medical images of the lungs to finally determine whether they are COVID-19 positive. As the number of patients with symptoms similar to pneumonia increases, more and more medical images of the lungs need to be generated. At the same time, the number of physicians at this stage is far from meeting the needs of patients, resulting in patients unable to detect and understand their own conditions in time. In this regard, we have performed image augmentation, data cleaning, and designed a deep learning classification network based on the data set of COVID-19 lung medical images. accurate classification judgment. The network can achieve 95.76% classification accuracy for this task through a new fine-tuning method and hyperparameter tuning we designed, which has higher accuracy and less training time than the classic convolutional neural network model. © 2023 SPIE.
COVID-19; image classification; medical imaging; self-attention; Classification (of information); Computer aided diagnosis; Computerized tomography; Convolutional neural networks; Deep learning; Patient treatment; Attention mechanisms; Common cold; Condition; CT Image; Disease prevention; Images classification; Model-based OPC; Pre-training; Training model
Texto completo:
Disponível
Coleções:
Bases de dados de organismos internacionais
Base de dados:
Scopus
Idioma:
Inglês
Revista:
Proceedings of SPIE - The International Society for Optical Engineering
Ano de publicação:
2023
Tipo de documento:
Artigo
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