| Texto completo | |
| Autor(es): Mostrar menos - |
Costa, Marcus V. L.
;
de Aguiar, Erikson J.
;
Rodrigues, Lucas S.
;
Ramos, Jonathan S.
;
Traina, Caetano, Jr.
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Traina, Agma J. M.
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Almeida, JR
;
Spiliopoulou, M
;
Andrades, JAB
;
Placidi, G
;
Gonzalez, AR
;
Sicilia, R
;
Kane, B
Número total de Autores: 13
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| Tipo de documento: | Artigo Científico |
| Fonte: | 2023 IEEE 36TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS; v. N/A, p. 6-pg., 2023-01-01. |
| Resumo | |
Medical image analysis plays a major role in aiding physicians in decision-making. Specifically in detecting COVID-19, Deep Learning (DL) and radiomic approaches have achieved promising results separately. However, DL results are hard to interpret/visualize, and the radiomic approach encompasses successive steps, such as image acquisition, image processing, segmentation, feature extraction, and analysis. In this paper, we integrate DL with radiomic approaches, aiding in detecting COVID-19. We use DL models to extract 128 relevant deep radiomic features to assess COVID-19 from several image sources of 392 representative chest X-ray (CXR) exams. We avoid successive radiomic steps by employing DL (transfer learning) from Imagenet's VGG-16, ResNet50V2, and DenseNet201 networks. We considered a set of Machine Learning (ML) algorithms to further validate our results, providing an ensemble model to detect COVID-19. Our experimental results show that our approach achieved 95% AUC using 128 relevant features from DenseNet201. Conversely, our ensemble model presented 91% AUC, indicating that deep learning-based radiomics could increase binary classification performance in a real scenario. In addition, we highlight that our approach can be adapted to create other DL-based radiomics tools. For reproducibility, we made our code available at https://github.com/usmarcv/CBMS-DL-based-radiomics. (AU) | |
| Processo FAPESP: | 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD) |
| Beneficiário: | Agma Juci Machado Traina |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 20/07200-9 - Analisando dados complexos vinculados a COVID-19 para apoio à tomada de decisão e prognóstico |
| Beneficiário: | Agma Juci Machado Traina |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 21/08982-3 - Segurança e privacidade em modelos de aprendizagem de máquina para imagens médicas contra ataques adversários |
| Beneficiário: | Erikson Júlio de Aguiar |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |