白血病是一种严重影响人类免疫系统的血液恶性肿瘤。早期发现有助于有效管理和治疗癌症。尽管深度学习技术有望实现血液疾病的早期检测，但其有效性往往受到可用数据集和部署设备的物理限制的限制。在这项研究中，我们收集了来自 85 名淋巴增殖性肿瘤患者的 17,826 个形态学骨髓细胞图像的高质量数据集。我们采用渐进式收缩方法，集成了跨多个维度（包括宽度、深度、分辨率和内核大小）的综合修剪技术来训练我们的轻量级模型。该模型实现了急性淋巴细胞白血病、慢性淋巴细胞白血病和其他骨髓细胞类型的快速识别，准确率高达 92.51%，吞吐量为每秒 111 张玻片，而仅包含 640 万个参数。该模型对白血病的诊断，特别是在淋巴系统疾病的快速、准确识别方面做出了重大贡献，并为提高医学专家诊断和治疗淋巴细胞白血病的效率和准确性提供了潜在的机会。© 2024 Wiley periodicals LLC。

Leukemia is a hematological malignancy that significantly impacts the human immune system. Early detection helps to effectively manage and treat cancer. Although deep learning techniques hold promise for early detection of blood disorders, their effectiveness is often limited by the physical constraints of available datasets and deployed devices. For this investigation, we collect an excellent-quality dataset of 17,826 morphological bone marrow cell images from 85 patients with lymphoproliferative neoplasms. We employ a progressive shrinking approach, which integrates a comprehensive pruning technique across multiple dimensions, including width, depth, resolution, and kernel size, to train our lightweight model. The proposed model achieves rapid identification of acute lymphoblastic leukemia, chronic lymphocytic leukemia, and other bone marrow cell types with an accuracy of 92.51% and a throughput of 111 slides per second, while comprising only 6.4 million parameters. This model significantly contributes to leukemia diagnosis, particularly in the rapid and accurate identification of lymphatic system diseases, and provides potential opportunities to enhance the efficiency and accuracy of medical experts in the diagnosis and treatment of lymphocytic leukemia.© 2024 Wiley Periodicals LLC.