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

doi:10.12140/j.issn.1000-7423.2026.04.022

Abstract

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

CLC Number:

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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