Identifying the Key Components in ResNet-50 for Diabetic Retinopathy Grading from Fundus Images: A Systematic Investigation

Although deep learning-based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on prediction performance. The training setting includes various interdependent com...

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Detalles Bibliográficos
Autores principales: Huang, Yijin, Lin, Li, Cheng, Pujin, Lyu, Junyan, Tam, Roger, Tang, Xiaoying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216935/
https://www.ncbi.nlm.nih.gov/pubmed/37238149
http://dx.doi.org/10.3390/diagnostics13101664

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10216935/
https://www.ncbi.nlm.nih.gov/pubmed/37238149
http://dx.doi.org/10.3390/diagnostics13101664

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