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.

翻译：自动化红细胞（RBC）分类技术通过分析血液涂片图像，能够以更短的时间和更低的成本辅助血液学家完成红细胞检测结果的分析。然而，重叠细胞会导致预测结果错误，因此必须在分类前将其分离为多个独立红细胞。在利用深度学习进行多类分类时，医学影像中常出现类别不平衡问题，因为正常样本数量通常远高于罕见疾病样本。本文提出一种从血液涂片图像中分割与分类红细胞的新方法，重点解决细胞重叠和数据不平衡问题。针对重叠细胞分离，我们的分割过程首先通过椭圆估计表征红细胞。该方法通过检测凹点，并采用定向椭圆拟合确定椭圆形状。基于20张血液涂片图像的实验显示，分割准确率为0.889。分类任务需要平衡的训练数据集，但部分红细胞类型较为罕见。本数据集中包含来自20,875个独立红细胞样本的12个红细胞类别，其不平衡比率达到34.538。因此，相较其他应用领域，基于不平衡数据集的机器学习红细胞分类更具挑战性。我们分析了应对该问题的技术方案。采用EfficientNet-B1网络结合数据增强方法后，取得最佳准确率0.921与F1分数0.8679。实验结果表明，通过提升少数类别的F1分数，权重平衡技术结合数据增强能有效处理不平衡问题，而数据增强则显著提升了整体分类性能。