The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermo...

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Detalles Bibliográficos
Autores principales: Li, Dana, Mikela Vilmun, Bolette, Frederik Carlsen, Jonathan, Albrecht-Beste, Elisabeth, Ammitzbøl Lauridsen, Carsten, Bachmann Nielsen, Michael, Lindskov Hansen, Kristoffer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6963966/
https://www.ncbi.nlm.nih.gov/pubmed/31795409
http://dx.doi.org/10.3390/diagnostics9040207

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6963966/
https://www.ncbi.nlm.nih.gov/pubmed/31795409
http://dx.doi.org/10.3390/diagnostics9040207

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