Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"

Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates" Please find below an explanation for the <strong>files </strong>in this repository: <br> <br> <strong>DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z</strong> Experimental data. To reproduce the analyses, unzip both files and put the content into a folder called "Dataset" <strong>02_CNN_PhenotypeClassif.7z</strong> CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder. <strong>03_ExampleMeasurement.zip</strong> One measurement file and a corresponding scatterplot <strong>04_Dataset_load.zip</strong> The python script "03_ExtractFeatures.py" loads the list of available experiment files (01_Dataset_Table_v02.csv). The experiment files are contained in DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z. The scrip then evaluates each experiment file to obtain distribution parameters for Area and Solidity. These values are written to new "01_Dataset_Table_v03.csv". <strong>05_RF_training</strong> Scripts to train and evaluate the Random Forest model (using features contained in "01_Dataset_Table_v03.csv"). <strong>07_pytranskit</strong> Scripts for training and evaluating CDT-PLDA classifier