  Automated red blood cell (RBC) classification on blood smear images helps
hematologists to analyze RBC lab results in a reduced time and cost. However,
overlapping cells can cause incorrect predicted results, and so they have to be
separated into multiple single RBCs before classifying. To classify multiple
classes with deep learning, imbalance problems are common in medical imaging
because normal samples are always higher than rare disease samples. This paper
presents a new method to segment and classify RBCs from blood smear images,
specifically to tackle cell overlapping and data imbalance problems. Focusing
on overlapping cell separation, our segmentation process first estimates
ellipses to represent RBCs. The method detects the concave points and then
finds the ellipses using directed ellipse fitting. The accuracy from 20 blood
smear images was 0.889. Classification requires balanced training datasets.
However, some RBC types are rare. The imbalance ratio of this dataset was
34.538 for 12 RBC classes from 20,875 individual RBC samples. The use of
machine learning for RBC classification with an imbalanced dataset is hence
more challenging than many other applications. We analyzed techniques to deal
with this problem. The best accuracy and F1-score were 0.921 and 0.8679,
respectively, using EfficientNet-B1 with augmentation. Experimental results
showed that the weight balancing technique with augmentation had the potential
to deal with imbalance problems by improving the F1-score on minority classes,
while data augmentation significantly improved the overall classification
performance.
