Uma visão sobre a classificação de pneumonia viral e bacteriana por radiografias de tórax
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1341Palavras-chave:
Inteligência Artificial, Radiografias de tórax, PneumoniaResumo
Objetivo: Este estudo apresenta uma revisão sistemática sobre o uso de Inteligência Artificial (IA), especialmente Deep Learning (DL), no diagnóstico e classificação da pneumonia por radiografias de tórax (RXT). Método: O estudo segue o protocolo PRISMA conduzindo a revisão em fases de identificação, triagem e análise de artigos da base Scopus. Resultados: A revisão recuperou 25 artigos relevantes entre 121 retornados e identificou crescente interesse científico pelo tema, além de avanços no diagnóstico, com alguns estudos alcançando até 99,7% acurácia no modelo proposto. Conclusão: A detecção precoce da pneumonia é essencial para um tratamento mais eficaz, e soluções que auxiliem especialistas são fundamentais. A literatura mostra que há uma evolução constante dessas soluções, embora ainda existam gargalos importantes a serem resolvidos.
Referências
Narayanan, B. N., Davuluru, V. S. P., et al. (2020, March). Two-stage deep learning architecture for pneumonia detection and its diagnosis in chest radiographs. In Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications (Vol. 11318, pp. 130-139). SPIE. DOI: https://doi.org/10.1117/12.2547635
Hu, S., Zhu, Y., Dong, D., et al. (2022). Chest Radiographs Using a Context-Fusion Convolution Neural Network (CNN): Can It Distinguish the Etiology of Community-Acquired Pneumonia (CAP) in Children?. Journal of Digital Imaging, 35(5), 1079-1090. DOI: https://doi.org/10.1007/s10278-021-00543-1
Ferreira, J. R., Cardenas, D. A. C., Moreno, R. A., et al. (2020, July). Multi-view ensemble convolutional neural network to improve classification of pneumonia in low contrast chest x-ray images. In 2020 42nd annual international conference of the IEEE engineering in Medicine & Biology Society (EMBC) (pp. 1238-1241). IEEE. DOI: https://doi.org/10.1109/EMBC44109.2020.9176517
Acharya, A. K., & Satapathy, R. (2020). A deep learning based approach towards the automatic diagnosis of pneumonia from chest radiographs. Biomedical and Pharmacology Journal, 13(1), 449-455. DOI: https://doi.org/10.13005/bpj/1905
Zhao, B., Liu, H., Zheng, C., et al. (2021). Image-based deep learning in diagnosing the etiology of pneumonia on pediatric chest X-rays. Pediatric Pulmonology, 56(5), 1036-1044. DOI: https://doi.org/10.1002/ppul.25229
Darici, M. B., Dokur, Z., & Olmez, T. (2020). Pneumonia detection and classification using deep learning on chest x-ray images. International Journal of Intelligent Systems and Applications in Engineering, 8(4), 177-183. DOI: https://doi.org/10.18201/ijisae.2020466310
Rajaraman, S., Candemir, S., Kim, I., et al.(2018). Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Applied Sciences, 8(10), 1715. DOI: https://doi.org/10.3390/app8101715
Garstka, J., & Strzelecki, M. (2020). Pneumonia detection in X-ray chest images based on convolutional neural networks and data augmentation methods. In 2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) (pp. 18-23). IEEE. DOI: https://doi.org/10.23919/SPA50552.2020.9241305
Polat, Ö., Ölmez, Z., & Ölmez, T. (2021). Determination of Pneumonia in X-ray Chest Images by Using Convolutional Neural Network. Turkish Journal of Electrical Engineering and Computer Sciences, 29(3), 1615-1627 DOI: https://doi.org/10.3906/elk-2009-1
Avolio, M., Fuduli, A., Vocaturo, E., et al.(2022). Multiple Instance Learning for viral pneumonia chest X-ray Classification. In SEBD (pp. 359-366).
Thanh, H. T., Yen, P. H., & Ngoc, T. B. (2021, March). Pneumonia classification in X-ray images using artificial intelligence technology. In 2020 Applying New Technology in Green Buildings (ATiGB) (pp. 25-30). IEEE. DOI: https://doi.org/10.1109/ATiGB50996.2021.9423017
Alsharif, R., Al-Issa, Y., Alqudah, A. M., et al.(2021). PneumoniaNet: Automated detection and classification of pediatric pneumonia using chest X-ray images and CNN approach. Electronics, 10(23), 2949. DOI: https://doi.org/10.3390/electronics10232949
Masud, M., Bairagi, A. K., Nahid, A. A., et al. (2021). A pneumonia diagnosis scheme based on hybrid features extracted from chest radiographs using an ensemble learning algorithm. Journal of Healthcare Engineering, 2021. DOI: https://doi.org/10.1155/2021/8862089
Vashisht, S., Lamba, S., Sharma, B., et al.(2023, May). Pneumonia Classification Model using Deep Learning Algorithm. In 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT) (pp. 249-253). IEEE. DOI: https://doi.org/10.1109/InCACCT57535.2023.10141688
Avola, D., Bacciu, A., Cinque, L., et al.(2022). Study on transfer learning capabilities for pneumonia classification in chest X-ray images. Computer Methods and Programs in Biomedicine, 221, 106833. DOI: https://doi.org/10.1016/j.cmpb.2022.106833
Khaled, M., Gaceb, D., Touazi, F., et al.(2022). Progressive and Combined Deep Transfer Learning for pneumonia diagnosis in chest X-ray images. In IDDM (pp. 160-173).
Ibrahim, A. U., Ozsoz, M., Serte, S., et al.(2021). Pneumonia classification using deep learning from chest X-ray images during COVID-19. Cognitive Computation, 1-13. DOI: https://doi.org/10.1007/s12559-020-09787-5
Hariri, M., & Avşar, E. (2023). COVID-19 and pneumonia diagnosis from chest X-ray images using convolutional neural networks. Network Modeling Analysis in Health Informatics and Bioinformatics, 12(1), 17. DOI: https://doi.org/10.1007/s13721-023-00413-6
Liu, J., Qi, J., Chen, W., et al.(2022). Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images. Computers in Biology and Medicine, 147, 105732. DOI: https://doi.org/10.1016/j.compbiomed.2022.105732
Sudarshan, V. K., A. Ramachandra, R., Tan, N. S. M. et al.(2022). VEntNet: Hybrid deep convolutional neural network model for automated multi‐class categorization of chest X‐rays. International Journal of Imaging Systems and Technology, 32(3), 778-797. DOI: https://doi.org/10.1002/ima.22715
Nillmani, Jain, P. K., Sharma, N., et al.(2022). Four types of multiclass frameworks for pneumonia classification and its validation in X-ray scans using seven types of deep learning artificial intelligence models. Diagnostics, 12(3), 652. DOI: https://doi.org/10.3390/diagnostics12030652
Bhosale, R. D., & Yadav, D. M. (2024). Customized convolutional neural network for pulmonary multi-disease classification using chest x-ray images. Multimedia Tools and Applications, 83(6), 18537-18571. DOI: https://doi.org/10.1007/s11042-023-16297-7
Jha, A., John, E., & Banerjee, T. (2022, August). Transfer Learning for COVID-19 and Pneumonia Detection using Chest X-Rays. In 2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 1-4). IEEE. DOI: https://doi.org/10.1109/MWSCAS54063.2022.9859403
Naseem, M. T., Hussain, T., Lee, C. S., et al.(2022). Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning. Sensors, 22(20), 7977. DOI: https://doi.org/10.3390/s22207977
Sarkar, O., Islam, M. R., Syfullah, M. K.,et al. (2023). Multi-scale CNN: An explainable ai-integrated unique deep learning framework for lung-affected disease classification. Technologies, 11(5), 134 DOI: https://doi.org/10.3390/technologies11050134
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.
