Classification of red blood cell shapes in flow using outlier tolerant machine learning
Fig 6
Image montage of all false negative (left) and false positive (right) classified croissants with respect to manual classification.
On the left, false negative croissants are shown, i.e. all cells being classified as croissant manually, but not by the neural network. In contrast, all cells classified as croissant shapes by automated analysis but not by hand are depicted in the right montage (false positive croissants). Numerical values given in the yellow box of each picture correspond to the respective output value of the CNN.