  Morphologies of red blood cells are normally interpreted by a pathologist. It
is time-consuming and laborious. Furthermore, a misclassified red blood cell
morphology will lead to false disease diagnosis and improper treatment. Thus, a
decent pathologist must truly be an expert in classifying red blood cell
morphology. In the past decade, many approaches have been proposed for
classifying human red blood cell morphology. However, those approaches have not
addressed the class imbalance problem in classification. A class imbalance
problem---a problem where the numbers of samples in classes are very
different---is one of the problems that can lead to a biased model towards the
majority class. Due to the rarity of every type of abnormal blood cell
morphology, the data from the collection process are usually imbalanced. In
this study, we aimed to solve this problem specifically for classification of
dog red blood cell morphology by using a Convolutional Neural Network (CNN)---a
well-known deep learning technique---in conjunction with a focal loss function,
adept at handling class imbalance problem. The proposed technique was conducted
on a well-designed framework: two different CNNs were used to verify the
effectiveness of the focal loss function and the optimal hyper-parameters were
determined by 5-fold cross-validation. The experimental results show that both
CNNs models augmented with the focal loss function achieved higher
$F_{1}$-scores, compared to the models augmented with a conventional
cross-entropy loss function that does not address class imbalance problem. In
other words, the focal loss function truly enabled the CNNs models to be less
biased towards the majority class than the cross-entropy did in the
classification task of imbalanced dog red blood cell data.
