寄生虫显微图像深度学习检测及应用研究进展

doi:10.12140/j.issn.1000-7423.2026.04.022

中国寄生虫学与寄生虫病杂志 ›› 2026, Vol. 44 ›› Issue (4): 608-614.doi: 10.12140/j.issn.1000-7423.2026.04.022

寄生虫显微图像深度学习检测及应用研究进展

刘秋池()(), 李松, 董力齐, 付子玉, 吴晓其, 李嗣龙, 刘贵洪, 姜美涵, 张浩^*()

齐齐哈尔医学院医学技术学院，黑龙江齐齐哈尔 161006

收稿日期:2026-03-31 修回日期:2026-07-24 出版日期:2026-08-30 发布日期:2026-08-19
通讯作者: * 张浩，男，博士，教授，从事人体寄生虫学研究。E-mail：805676596@qq.com
作者简介:刘秋池（ORCID：0009-0000-4804-7981），女，本科生，从事深度学习在寄生虫显微图像检测中的应用研究。 E-mail：1399529159@qq.com
作者贡献
刘秋池负责文献检索、论文修改，李松负责数据整理、初稿校对，董力齐负责文献筛选、内容逻辑梳理，付子玉负责图像处理，吴晓其负责补充外文文献、内容查漏，李嗣龙负责框架优化、初稿审阅，刘贵洪负责参考文献整理，姜美涵负责稿件通读、文字润色，张浩负责选题设计、全文终审。
基金资助:
黑龙江省大学生创新创业训练计划省级项目(S202511230012)

Research progress on deep learning-based detection of parasite microimages and its applications

LIU Qiuchi()(), LI Song, DONG Liqi, FU Ziyu, WU Xiaoqi, LI Silong, LIU Guihong, JIANG Meihan, ZHANG Hao^*()

School of Medical Technology, Qiqihar Medical University, Qiqihar 161006, Heilongjiang, China

Received:2026-03-31 Revised:2026-07-24 Online:2026-08-30 Published:2026-08-19
Contact: * E-mail: 805676596@qq.com
Supported by:
Provincial College Students’ Innovation and Entrepreneurship Training Program of Heilongjiang Province(S202511230012)

摘要/Abstract

摘要：

寄生虫识别是医学诊断与公共卫生领域中的重要任务，图像分割的准确性直接影响检测结果的可靠性。近年来，深度学习技术发展迅速，在医学图像分析领域展现出巨大应用潜力，其较传统人工镜检方法具有效率高、客观性强、一致性好等显著优势。本文对卷积神经网络（CNN）、高效卷积神经网络（EfficientNet）、YOLOv8、嵌套U型网络（U-Net++）、Transformer等深度学习模型在寄生虫图像处理中的应用进行归纳，总结其结构特征、性能表现及适用情境，同时分析现有研究中普遍存在的问题。

关键词: 寄生虫识别, 图像分割, 深度学习, 医学图像分析

Abstract:

Parasite identification is a critical task in the medical diagnosis and public health field. The precision of image segmentation directly determines the reliability of detection results. Recently, deep-learning technologies have developed rapidly and shown great potential in medical image analysis. Deep learning-based tools are superior to conventional manual microscopy in terms of efficiency, objectivity and consistency. This review summarizes the applications of deep-learning models in parasite image processing, including Convolutional Neural Network (CNN), Efficient Convolutional Network (EfficientNet), You Only Look Once version 8 (YOLOv8), ested U-Net (U-Net++), and Transformer, describes the structural features, performance metrics and applicable scenarios of these models, and analyzes the problems encountered in current relevant studies.

Key words: Parasite recognition, Image segmentation, Deep learning, Medical image analysis

中图分类号:

刘秋池, 李松, 董力齐, 付子玉, 吴晓其, 李嗣龙, 刘贵洪, 姜美涵, 张浩. 寄生虫显微图像深度学习检测及应用研究进展[J]. 中国寄生虫学与寄生虫病杂志, 2026, 44(4): 608-614.

LIU Qiuchi, LI Song, DONG Liqi, FU Ziyu, WU Xiaoqi, LI Silong, LIU Guihong, JIANG Meihan, ZHANG Hao. Research progress on deep learning-based detection of parasite microimages and its applications[J]. Chinese Journal of Parasitology and Parasitic Diseases, 2026, 44(4): 608-614.

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寄生虫显微图像深度学习检测及应用研究进展

Research progress on deep learning-based detection of parasite microimages and its applications

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