CovNet-UFCSPA: auxiliando no diagnóstico de pneumonia por coronavírus
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1377Palavras-chave:
COVID-19, Aprendizado Profundo, CNNResumo
Objective: This study introduces the CovNet-UFCSPA architecture, which incorporates pre-processing data from clinical images (X-rays) and deep learning algorithms. Method: A total of 24,235 images were used for training, validation, and testing of the model, identifying areas in the X-rays that influence the model's decision. Result: The architecture achieved a recall of 99% in classifying X-rays from patients at the Hospital de Clínicas de Porto Alegre (HCPA). The application of the CLAHE technique improved the region of interest in the X-rays, reducing the false negative rate from 187 to 9. Conclusion: Compared with Resnet50 V2 and Inception V3 architectures, CovNet-UFCSPA demonstrated superiority in false negative rates, true positives, and recall.
Referências
Gong J, Dong H, Xia SQ, Huang YZ, Wang D, Zhao Y, Liu W, Tu S, Zhang M, Wang Q, et al. Correlation analysis between disease severity and inflammation-related parameters in patients with COVID-19 pneumonia. MedRxiv. 2020. DOI: https://doi.org/10.1101/2020.02.25.20025643
Udugama B, Kadhiresan P, Kozlowski HN, Malekjahani A, Osborne M, Li VYC, Chen H, Mubareka S, Gubbay JB, Chan WCW. Diagnosing COVID-19: the disease and tools for detection. ACS Nano. 2020;14(4):3822-3835. DOI: https://doi.org/10.1021/acsnano.0c02624
DATASUS. Equipments of Imaging Used in Health - E - DATASUS. DATASUS. Available at: http://tabnet.datasus.gov.br/tabdata/LivroIDB/2edrev/e18.pdf
Chassagnon G, Vakalopoulou M, Paragios N, Revel MP. Artificial intelligence applications for thoracic imaging. Eur J Radiol. 2020;123:108774. DOI: https://doi.org/10.1016/j.ejrad.2019.108774
Nahid AA, Sikder N, Bairagi AK, Razzaque M, Masud M, Kouzani AZ, Mahmud MA, et al. A novel method to identify pneumonia through analyzing chest radiographs employing a multichannel convolutional neural network. Sensors. 2020;20(12):3482. DOI: https://doi.org/10.3390/s20123482
Rajaraman S, Antani S. Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection. medRxiv. 2020. DOI: https://doi.org/10.1101/2020.05.04.20090803
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020;121:103792. DOI: https://doi.org/10.1016/j.compbiomed.2020.103792
Mittal A, Singh K, Misra DP. Detecting COVID-19 using ResNet deep learning model with X-ray images. Biocybernetics and Biomedical Engineering. 2020.
Takara, K., Nishiyama, Y., & Sone, S. (2022). Artificial Intelligence System for Chest X-ray Diagnosis of COVID-19: Development and Validation Study. Journal of Medical Internet Research, 24(1), e30527.
Nouara Cândida Xavier, Tathiane Alves Pianoschi Alva, Carla Diniz Lopes Becker. Ciências da Saúde: uma abordagem holística. Editora Conhecimento Livre; 2022. Cap 5.
Gonzalez, Rafael C., and Richard E. Woods. Processamento de imagens digitais. Editora Blucher, 2000.
Chollet F. Deep learning with Python. Simon and Schuster; 2021.
Yamashita R, Nishio M, Do RK, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights into Imaging. 2018;9(4):611-629. DOI: https://doi.org/10.1007/s13244-018-0639-9
O'Shea K, Nash R. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458. 2015.
Albawi S, Mohammed TA, Al-Zawi S. Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET). IEEE; 2017. pp. 1-6. DOI: https://doi.org/10.1109/ICEngTechnol.2017.8308186
scikit. Sklearn.utils.class_weight.compute_class_weight. [Online]. Available in: https://scikit-learn.org/stable/modules/generated/sklearn.utils.class_weight.compute_class_weight.html. Access at: 2024.
Cross-validation: evaluating estimator performance. [Online]. Available in: https://scikit-learn.org/stable/modules/cross_validation.html. Access at: 2024
Downloads
Publicado
Como Citar
Edição
Secção
Licença
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição-NãoComercial-CompartilhaIgual 4.0.
A submissão de um artigo ao Journal of Health Informatics é entendida como exclusiva e que não está sendo considerada para publicação em outra revista. A permissão dos autores para a publicação de seu artigo no J. Health Inform. implica na exclusiva autorização concedida aos editores para incluí-lo na revista. Ao submeter um artigo, ao autor será solicitada a permissão eletrônica de um Termo de Transferência de Direitos Autorais. Uma mensagem eletrônica será enviada ao autor correspondente confirmando o recibo do manuscrito e o aceite da Declaração de Direito Autoral.
