Machine learning approach of automatic identification and counting of blood cells (RBC, WBC, and Platelet) with KNN and IOU based verification.
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Updated
Dec 21, 2022 - Python
Machine learning approach of automatic identification and counting of blood cells (RBC, WBC, and Platelet) with KNN and IOU based verification.
clotFoam_sd provides a general framework for simulating shear-dependent platelet-mediated coagulation in OpenFOAM
Test to count the number of platelets, red blood cells (RBCs), and white blood cells (WBCs) in blood sample photos, using https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3 as the training and test dataset.
Factibilidad del stock de plaquetas en redes de hemoterapia distribuidas: cota de perecibilidad (ley de Little), conciliación del balance de masa y motor de decisión diaria.
Platelet function by flow cytometry in essential thrombocythemia: aggregation, surface markers and hemogram analysis in R. Commissioned analysis code (2021) published as delivered and cleaned, with documented limitations and no patient data.
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