快速准确的血涂片分析对于诊断白血病和其他血液恶性肿瘤至关重要。然而，手动白细胞计数和形态学评估仍然耗时且容易出错。此外，由于恶性和良性细胞形态之间的视觉相似性，传统的图像处理方法很难区分细胞。针对上述挑战，我们提出了用于白血病分类的耦合变压器卷积网络（CoTCoNet）框架。 CoTCoNet 集成了双特征提取来捕获远程全局特征和细粒度空间模式，有助于识别复杂的血液学特征。此外，该框架采用基于图形的模块来揭示白细胞隐藏的生物学相关特征，并采用基于群体的元启发式算法来进行特征选择和优化。此外，我们引入了白细胞分割和合成的新颖组合，它隔离相关区域，同时用真实的白细胞样本增强训练数据集。该策略隔离相关区域，同时用真实的白细胞样本增强训练数据，增强特征提取，并在不影响数据完整性的情况下解决数据稀缺问题。我们在包含 16,982 个带注释的细胞的数据集上评估了 CoTCoNet，实现了 0.9894 的准确度和 0.9894 的 F1 分数0.9893。我们在四个不同的公开数据集（包括上述数据集）上测试了 CoTCoNet，以评估普遍性。结果表明，与现有最先进方法相比，性能有了显着提高。CoTCoNet 代表了白血病分类方面的重大进步，与传统方法相比，准确性和效率更高。通过整合与细胞注释紧密结合的可解释可视化，该框架提供了对其决策过程的更深入见解，进一步巩固了其在临床环境中的潜力。版权所有 © 2024 Elsevier Ltd。保留所有权利。

Swift and accurate blood smear analyses are crucial for diagnosing leukemia and other hematological malignancies. However, manual leukocyte count and morphological evaluation remain time-consuming and prone to errors. Additionally, conventional image processing methods struggle to differentiate cells due to visual similarities between malignant and benign cell morphology.In response to above challenges, we propose Coupled Transformer Convolutional Network (CoTCoNet) framework for leukemia classification. CoTCoNet integrates dual-feature extraction to capture long-range global features and fine-grained spatial patterns, facilitating the identification of complex hematological characteristics. Additionally, the framework employs a graph-based module to uncover hidden, biologically relevant features of leukocyte cells, along with a Population-based Meta-Heuristic Algorithm for feature selection and optimization. Furthermore, we introduce a novel combination of leukocyte segmentation and synthesis, which isolates relevant regions while augmenting the training dataset with realistic leukocyte samples. This strategy isolates relevant regions while augmenting the training data with realistic leukocyte samples, enhancing feature extraction, and addressing data scarcity without compromising data integrity.We evaluated CoTCoNet on a dataset of 16,982 annotated cells, achieving an accuracy of 0.9894 and an F1-Score of 0.9893. We tested CoTCoNet on four diverse, publicly available datasets (including those above) to assess generalizability. Results demonstrate a significant performance improvement over existing state-of-the-art approaches.CoTCoNet represents a significant advancement in leukemia classification, offering enhanced accuracy and efficiency compared to traditional methods. By incorporating explainable visualizations that closely align with cell annotations, the framework provides deeper insights into its decision-making process, further solidifying its potential in clinical settings.Copyright © 2024 Elsevier Ltd. All rights reserved.