Accuracy of Tested DCNN Models for T. brucei classification.

Saved in:

Bibliographic Details
Main Author:	Hamid Cheraghi (22057476) (author)
Other Authors:	Lara López-Escobar (22057479) (author), José Rino (81063) (author), Luisa M. Figueiredo (7841315) (author), Bálint Szabó (235162) (author)
Published:	2025
Subjects:	Medicine Microbiology Cell Biology Sociology Space Science Biological Sciences not elsewhere classified Physical Sciences not elsewhere classified Information Systems not elsewhere classified understanding parasite biology two key stages segmentation step using outperforming standard architectures flourescent transgenic parasites extracts individual parasites unlabeled microscopic images parasite stage classification classification step utilizing div >< p trypanosoma brucei </ brucei </ unlabeled morphology brucei ,</ classification model accurate classification xception architecture transmission dynamics transfer learning throughput analysis results demonstrate pipeline consists learning pipeline highly challenging forms visually efficient approach deep learning cellpose algorithm bloodstream forms biomedical imaging automated deep
Tags:	Add Tag No Tags, Be the first to tag this record!

Accuracy of Tested DCNN Models for <i><i>T. brucei</i></i> classification.