Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin
Fig 8
Manual vs machine learning (ML) performance.
Results from pitting count estimates from 19 whole microchannel images processed through our automated two-part processing pipeline vs. manual characterization. Error bars along the manual axis are obtained from variance in repeated manual counts on a set of test images. The red line is the line of perfect agreement. Error bars on ML counts are estimated from the precision rates reached by our Phase II classifier network in predicting true positive outcomes in relevant categories on a validation set (see Fig 6). R2 statistic values, indicating goodness of agreement between manual and ML counts, are indicated in each graph. A: Results for total sRBC (deformable + nondeformable) cell counts. This plot is illustrative of the high degree of accuracy achieved by our ML in identifying sRBCs. B and C: Results for number of sRBCs in each channel image classified manually and by ML as deformable or non-deformable respectively. This measures the agreement reached in classification of the two morphological categories.