高精度、轻量级的图像分类网络用于优化淋巴母细胞白血病诊断
High-Accuracy and Lightweight Image Classification Network for Optimizing Lymphoblastic Leukemia Diagnosisy
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影响因子:2.1
分区:工程技术3区 / 显微镜技术2区 解剖学与形态学3区 生物学4区
发表日期:2025 Feb
作者:
Liye Mei, Chentao Lian, Suyang Han, Shuangtong Jin, Jing He, Lan Dong, Hongzhu Wang, Hui Shen, Cheng Lei, Bei Xiong
DOI:
10.1002/jemt.24704
摘要
白血病是一种严重影响人体免疫系统的血液恶性肿瘤。早期检测有助于有效管理和治疗癌症。虽然深度学习技术在血液疾病早期检测方面具有潜力，但其效果常受到可用数据集和设备的物理限制。本研究收集了由85名淋巴增生性肿瘤患者提供的17,826张高质量骨髓细胞形态学图像数据集。我们采用逐步缩减方法，结合多维度的剪枝技术，包括宽度、深度、分辨率和核大小，训练我们轻量级模型。该模型实现了对急性淋巴细胞白血病、慢性淋巴细胞白血病及其他骨髓细胞类型的快速识别，准确率达92.51%，每秒处理111个切片，仅包含640万参数。该模型在白血病诊断中具有显著贡献，尤其是在快速、准确识别淋巴系统疾病方面，为提升医疗专家在淋巴细胞白血病诊断与治疗的效率和准确性提供了潜在的机会。
Abstract
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.