In recent years, deep learning techniques have been increasingly utilized to automate and reduce human errors in leukocyte classification. However, reliability and accuracy issues remain a challenge. This paper proposes CAB‐Net, an end‐to‐end deep learning‐based model that integrates a convolutional neural network with an attention mechanism and BLSTM. The model uses a stack of convolutional and pooling layers to extract initial features from input images, which are then fed into the attention block to generate class‐specific discriminative features and BLSTM employed to model deterministic class correlations between highlighted discriminative features and labels. Experimental results show that CAB‐Net outperforms state‐of‐the‐art methods in term of accuracy, precision, recall, F1‐Score, and AUC‐ROC. In addition, the CAB‐Net model demonstrates good performance even with low‐resolution images. Consequently, CAB‐Net can be considered as the current state‐of‐the‐art prediction model for improving leukocyte classification in clinical environments.

中文翻译：

---

CAB-Net：用于增强白细胞分类的卷积注意力 BLSTM 网络

近年来，深度学习技术越来越多地用于白细胞分类的自动化和减少人为错误。然而，可靠性和准确性问题仍然是一个挑战。本文提出了 CAB-Net，这是一种基于端到端深度学习的模型，它将卷积神经网络与注意力机制和 BLSTM 集成在一起。该模型使用一堆卷积层和池化层从输入图像中提取初始特征，然后将其输入注意块中以生成特定于类的判别特征，并使用 BLSTM 来对突出显示的判别特征和标签之间的确定性类别相关性进行建模。实验结果表明，CAB-Net 在准确度、精确度、召回率、F1-Score 和 AUC-ROC 方面优于最先进的方法。此外，即使对于低分辨率图像，CAB-Net 模型也表现出良好的性能。因此，CAB-Net 可以被视为当前最先进的预测模型，用于改善临床环境中的白细胞分类。