生态学报2025，Vol.45Issue(7)：3534-3543,10.DOI:10.20103/j.stxb.202405151099
浮游植物类别不均衡图像分类方法对比研究
Comparative study of class-imbalanced image classification algorithms for phytoplankton
摘要
Abstract
The distribution of phytoplankton classes in freshwater in imbalanced,with collected microscopic images containing significantly more samples of advantaged classes than of disadvantaged items.General deep-learning-based image classification methods trained on such datasets generally perform poorly in classifying disadvantaged classes.In addressing the classification errors caused by the class-imbalanced phytoplankton dataset in deep learning model,various solutions for handing this issue in macro-domain have been analyzed.The practicality of these methods in the domain of microscopic images of phytoplankton is explored.A dataset consisting of 29 genera and 18044 images from Lake Chaohu was collected,constructing a microscopic image dataset of phytoplankton with class-imbalanced problem.An evaluation of the model's classification abilities was proposed using both micro-average and macro-average metrics.Experimental results indicate that the model trained by general method performs lower F1 values when predicting samples from disadvantaged classes.Conversely,the model trained by the square-root sampling method in the re-sampling major category exhibit significant improvement in both micro-average and macro-average metrics,with F1 values reaching 0.932 and 0.852,respectively.Particularly,on the top 10 disadvantaged genera,the F1 values for micro-average and macro-average increased by 9.64％and 15.94％,respectively.This study provides an effective method for training deep learning model for the automated detection of phytoplankton community structure in freshwater.关键词
浮游植物/显微/深度学习/类别不均衡/图像分类Key words
phytoplankton/microscopic/deep learning/class imbalanced/image classification引用本文复制引用
梁天泓,黄朋,董鸣,陈晓伟,殷高方,邵新童,赵南京,张小玲,贾仁庆,徐敏,张子豪,胡翔..浮游植物类别不均衡图像分类方法对比研究[J].生态学报,2025,45(7):3534-3543,10.基金项目
安徽省科技重大专项(202203a07020002) （202203a07020002）
合肥综合性科学中心环境研究院科研团队建设项目(HYKYTD2024004) （HYKYTD2024004）
安徽省生态环境科研项目(2023hb0011) （2023hb0011）
中国科学院合肥物质科学研究院院长基金(YZJJ2024QN01) （YZJJ2024QN01）
