White Blood Cells play an important role in observing the health condition of an individual. The opinion related to blood disease involves the identification and characterization of a patient's blood sample. Recent approaches employ Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and merging of CNN and RNN models to enrich the understanding of image content. From beginning to end, training of big data in medical image analysis has encouraged us to discover prominent features from sample images. A single cell patch extraction from blood sample techniques for blood cell classification has resulted in the good performance rate. However, these approaches are unable to address the issues of multiple cells overlap. To address this problem, the Canonical Correlation Analysis (CCA) method is used in this paper. CCA method views the effects of overlapping nuclei where multiple nuclei patches are extracted, learned and trained at a time. Due to overlapping of blood cell images, the classification time is reduced, the dimension of input images gets compressed and the network converges faster with more accurate weight parameters. Experimental results evaluated using publicly available database show that the proposed CNN and RNN merging model with canonical correlation analysis determines higher accuracy compared to other state-of-the-art blood cell classification techniques.

白细胞在观察个人健康状况方面起着重要作用。与血液疾病相关的意见涉及患者血液样本的识别和表征。最近的方法采用卷积神经网络 (CNN)、循环神经网络 (RNN) 以及 CNN 和 RNN 模型的合并来丰富对图像内容的理解。从始至终，医学图像分析中的大数据训练都鼓励我们从样本图像中发现突出的特征。从血液样本中提取单细胞贴片用于血细胞分类的技术具有良好的性能。然而，这些方法无法解决多个单元重叠的问题。为了解决这个问题，本文使用了典型相关分析（CCA）方法。CCA 方法查看重叠核的影响，其中一次提取、学习和训练多个核补丁。由于血细胞图像的重叠，分类时间减少，输入图像的维度被压缩，网络收敛更快，权重参数更准确。使用公开可用数据库评估的实验结果表明，与其他最先进的血细胞分类技术相比，所提出的具有典型相关分析的 CNN 和 RNN 合并模型确定了更高的准确性。输入图像的维度被压缩，网络收敛速度更快，权重参数更准确。使用公开可用数据库评估的实验结果表明，与其他最先进的血细胞分类技术相比，所提出的具有典型相关分析的 CNN 和 RNN 合并模型确定了更高的准确性。输入图像的维度被压缩，网络收敛速度更快，权重参数更准确。使用公开可用数据库评估的实验结果表明，与其他最先进的血细胞分类技术相比，所提出的具有典型相关分析的 CNN 和 RNN 合并模型确定了更高的准确性。