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：血液涂片图像的自动红血细胞(RBC)分类(RBC)在血液涂片图像上的自动红血细胞(RBC)分类有助于血液学家分析RBC实验室在时间和成本上减少的结果。然而,重叠细胞可以造成不正确的预测结果,因此在分类之前,必须将其分为多个单一的RBC。要对多个班进行深层学习,在医疗成像中,不平衡问题很常见,因为正常的样本总是高于罕见的疾病样本。本文介绍了将RBC从血液涂片中分类和分类的一种新的方法,具体是为了解决细胞重叠和数据不平衡问题。侧重于细胞分离,我们分解过程首先估计的是代表RBC的省略结果。这种方法可以探测混结点,然后发现使用定向的椭圆的剪贴图进行分类。20个血涂图的准确性为0.889,分类需要平衡的培训数据集的种类很少。该数据集的偏差比例为34.538,而来自20 875个RBC个人RBC样本的样本。使用机器对不平衡数据进行分类的分类,因此比其他许多应用程序更具挑战性。我们用这种方法来检测这些方法来检测和查找点点点点点点点点点点,然后找到使用精确点点点点点点点点点点,我们用直点分析了使用这种方法来处理这个方法来处理这个方法,用0.8-B的精度,同时用0.8-B的精确度, 递增方法来处理这个方法处理这个方法分别的递增率。