Cite
Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the clinical images melanoma detection problem
MLA
Luigi Di Biasi, et al. “Refactoring and Performance Analysis of the Main CNN Architectures: Using False Negative Rate Minimization to Solve the Clinical Images Melanoma Detection Problem.” BMC Bioinformatics, vol. 24, no. 1, Oct. 2023, pp. 1–19. EBSCOhost, https://doi.org/10.1186/s12859-023-05516-5.
APA
Luigi Di Biasi, Fabiola De Marco, Alessia Auriemma Citarella, Modesto Castrillón-Santana, Paola Barra, & Genoveffa Tortora. (2023). Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the clinical images melanoma detection problem. BMC Bioinformatics, 24(1), 1–19. https://doi.org/10.1186/s12859-023-05516-5
Chicago
Luigi Di Biasi, Fabiola De Marco, Alessia Auriemma Citarella, Modesto Castrillón-Santana, Paola Barra, and Genoveffa Tortora. 2023. “Refactoring and Performance Analysis of the Main CNN Architectures: Using False Negative Rate Minimization to Solve the Clinical Images Melanoma Detection Problem.” BMC Bioinformatics 24 (1): 1–19. doi:10.1186/s12859-023-05516-5.