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Publication details

Document type
Journal articles

Document subtype
Full paper

Title
You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction

Participants in the publication
João Mendes (Author)
Dep. Física
Unidade de I&D e Inovação
LASIGE
Bernardo Oliveira (Author)
Carolina Araújo (Author)
Joana Galrão (Author)
Nuno C. Garcia (Author)
Dep. Informática
LASIGE
Nuno Matela (Author)
Dep. Física
IBEB

Date of Publication
2025-03

Where published
Computers in Biology and Medicine

Publication Identifiers
ISSN - 0010-4825

Publisher
Elsevier BV

Volume
187

Starting page
109733

Document Identifiers
DOI - https://doi.org/10.1016/j.compbiomed.2025.109733
URL - https://doi.org/10.1016/j.compbiomed.2025.109733

Rankings
Web Of Science Q1 (2023) - 7.0 - ENGINEERING, BIOMEDICAL
SCOPUS Q1 (2023) - 11.7 - Health Informatics
SCOPUS Q1 (2023) - 11.7 - Computer Science Applications
Web Of Science Q1 (2023) - 7.0 - COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
SCIMAGO Q1 (2023) - 1.481 - Health Informatics
SCIMAGO Q1 (2023) - 1.481 - Computer Science Applications


Export

APA
João Mendes, Bernardo Oliveira, Carolina Araújo, Joana Galrão, Nuno C. Garcia, Nuno Matela, (2025). You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction. Computers in Biology and Medicine, 187, ISSN 0010-4825. eISSN . https://doi.org/10.1016/j.compbiomed.2025.109733

IEEE
João Mendes, Bernardo Oliveira, Carolina Araújo, Joana Galrão, Nuno C. Garcia, Nuno Matela, "You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction" in Computers in Biology and Medicine, vol. 187, 2025. 10.1016/j.compbiomed.2025.109733

BIBTEX
@article{63640, author = {João Mendes and Bernardo Oliveira and Carolina Araújo and Joana Galrão and Nuno C. Garcia and Nuno Matela}, title = {You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction}, journal = {Computers in Biology and Medicine}, year = 2025, volume = 187 }