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. Author manuscript; available in PMC: 2016 Oct 1.
Published in final edited form as: Vox Sang. 2015 Apr 20;109(3):221–230. doi: 10.1111/vox.12277

Figure 4.

Figure 4

Confusion matrix for the automated stored RBC morphology classification algorithm. Test set consisted of 1000 individual stored RBCs randomly selected from the entire set. Rows correspond to true RBC classes (categorized manually by eye) and columns correspond to algorithm-predicted RBC classes (categorized automatically by algorithm). Cells along the diagonal contain correct high-resolution morphology classifications, and all shaded cells contain correct low-resolution morphology classifications. The overall low-resolution classification accuracy of the algorithm is 91.9%, and the overall high-resolution classification accuracy of the algorithm is 75.3%.