Macroscopic aspergillus recognition using YOLO-CSM

Wenjie Zhao, Zhenhua Ma, Huiying Ru and Xinhang Zhang

PLOS ONE, 2026, vol. 21, issue 8, 1-15

Abstract: Aspergillus detection is of great significance in medical diagnosis and microbiological analysis. However, due to the complex morphology of Aspergillus, its subtle structural characteristics, the presence of rare strains, and partial occlusion in samples, traditional detection methods often fail to achieve high accuracy. To address these challenges, this study incorporates three innovative modules—CGNet, Shape-IoU, and MSCA—into the YOLOv12 framework to construct an efficient Aspergillus detection model. CGNet enhances the fusion and representation of complex structural features; Shape-IoU introduces morphological constraints to improve matching accuracy in scenarios with occlusion and blurred boundaries; and MSCA strengthens the model’s adaptability to strains of different scales. Experimental results show that the proposed model achieves a Precision of 92.1%, Recall of 87.8%, and mAP of 89.2% in Aspergillus detection, while requiring only 28.6M parameters, effectively balancing accuracy and lightweight design. These results demonstrate the superiority of the proposed method.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0356242 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 56242&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0356242

DOI: 10.1371/journal.pone.0356242

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().