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

Convolutional Neural Networks based Focal Loss for Class Imbalance Problem: A Case Study of Canine Red Blood Cells Morphology Classification