2026-10-15T11:13:46Z https://data.mendeley.com/oai

oai:data.mendeley.com/j55fyhtxn4.2 2021-03-09T11:33:09Z

Baum, Jake PlasmoCount: Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks Mendeley Data PlasmoCount is an online tool for automated detection of infected red blood cells by Plasmodium (malaria) parasites. The tool was built using a machine learning approach (convolutional neural networks) developed from training data sets of parasite infected smears (Giemsa-smears) and then subsequently tested on blind data sets to validate accuracy and reproducibility. The data sets behind the work (first described in the preprint of our paper on MedRxiv - https://doi.org/10.1101/2021.01.26.21250284) are supplied here to enable others to build improved models or alternative strategies to automated cell detection. More information about the data sets or model can be gained from contacting the Baum laboratory. Infectious Disease Cell Biology Machine Learning Malaria Diagnostics Plasmodium Convolutional Neural Network Cellular Imaging Davidson, Mira Dataset doi:10.17632/j55fyhtxn4.2 oai:data.mendeley.com/j55fyhtxn4.2 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/j55fyhtxn4 2021-03-09T11:33:09Z