高准确性和轻巧的图像分类网络，用于优化淋巴细胞白血病诊断
High-Accuracy and Lightweight Image Classification Network for Optimizing Lymphoblastic Leukemia Diagnosisy
影响因子:2.10000
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
摘要
白血病是一种血液学恶性肿瘤，对人免疫系统产生了重大影响。早期检测有助于有效管理和治疗癌症。尽管深度学习技术有望早日检测血液疾病，但它们的有效性通常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例淋巴增生性肿瘤患者的17,826个形态骨髓细胞图像的出色质量数据集。我们采用了一种渐进式收缩方法，该方法将跨多个维度（包括宽度，深度，分辨率和内核大小）跨越的全面修剪技术整合在一起，以训练我们的轻量级模型。提出的模型可快速鉴定急性淋巴细胞白血病，慢性淋巴细胞性白血病和其他骨髓细胞类型，精度为92.51％，每秒111个幻灯片的吞吐量，而仅包含640万参数。该模型极大地有助于白血病诊断，尤其是在快速，准确地鉴定淋巴系统疾病的过程中，并为提高医学专家在诊断和治疗淋巴细胞性白血病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.