Swift and accurate blood smear analysis is an effective diagnostic method for leukemia and other hematological malignancies. However, manual leukocyte count and morphological evaluation using a microscope is time-consuming and prone to errors. Conventional image processing methods also exhibit limitations in differentiating cells due to the visual similarity between malignant and benign cell morphology. This limitation is further compounded by the skewed training data that hinders the extraction of reliable and pertinent features. In response to these challenges, we propose an optimized Coupled Transformer Convolutional Network (CoTCoNet) framework for the classification of leukemia, which employs a well-designed transformer integrated with a deep convolutional network to effectively capture comprehensive global features and scalable spatial patterns, enabling the identification of complex and large-scale hematological features. Further, the framework incorporates a graph-based feature reconstruction module to reveal the hidden or unobserved hard-to-see biological features of leukocyte cells and employs a Population-based Meta-Heuristic Algorithm for feature selection and optimization. To mitigate data imbalance issues, we employ a synthetic leukocyte generator. In the evaluation phase, we initially assess CoTCoNet on a dataset containing 16,982 annotated cells, and it achieves remarkable accuracy and F1-Score rates of 0.9894 and 0.9893, respectively. To broaden the generalizability of our model, we evaluate it across four publicly available diverse datasets, which include the aforementioned dataset. This evaluation demonstrates that our method outperforms current state-of-the-art approaches. We also incorporate an explainability approach in the form of feature visualization closely aligned with cell annotations to provide a deeper understanding of the framework.

翻译：快速而准确的血涂片分析是诊断白血病及其他血液恶性肿瘤的有效方法。然而，使用显微镜进行人工白细胞计数和形态学评估耗时且易出错。由于恶性与良性细胞形态在视觉上的相似性，传统图像处理方法在区分细胞方面也存在局限。训练数据的偏态分布进一步加剧了这一局限，阻碍了可靠且相关特征的提取。针对这些挑战，我们提出了一种用于白血病分类的优化耦合Transformer-卷积网络（CoTCoNet）框架。该框架将精心设计的Transformer与深度卷积网络相结合，以有效捕获全面的全局特征和可扩展的空间模式，从而实现对复杂、大规模血液学特征的识别。此外，框架还引入了基于图的特征重构模块，以揭示白细胞细胞中隐藏或难以观察的生物特征，并采用基于群体的元启发式算法进行特征选择与优化。为缓解数据不平衡问题，我们使用了合成白细胞生成器。在评估阶段，我们首先在包含16,982个标注细胞的数据集上测试CoTCoNet，其取得了0.9894的准确率和0.9893的F1分数，表现优异。为拓宽模型的泛化能力，我们在四个公开的多样化数据集（包含上述数据集）上进行了评估。评估结果表明，我们的方法优于当前最先进的技术。我们还引入了一种可解释性方法，通过紧密贴合细胞标注的特征可视化来提供对框架的深入理解。