Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin
Table 1
Details of data sets used for training / validating the neural networks in the two phases of our workflow.
For both Phase I and II, we use k-fold cross validation with k = 5, and split the respective data sets so that the training and validation sets correspond to approximately 80% and 20% of the whole dataset for each fold.