分类器(UML)
人工智能
计算机科学
模式识别(心理学)
机器学习
花粉
工作量
随机森林
微流控
材料科学
生物
纳米技术
生态学
操作系统
作者
Michele D’Orazio,Riccardo Reale,Adele De Ninno,Maria Antonia Brighetti,Arianna Mencattini,Luca Businaro,Eugenio Martinelli,Paolo Bisegna,Alessandro Travaglini,Federica Caselli
出处
期刊:IEEE Transactions on Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-02-01
卷期号:69 (2): 921-931
被引量:18
标识
DOI:10.1109/tbme.2021.3109384
摘要
In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches.We propose a new multimodal approach that combines electrical sensing and optical imaging to classify pollen grains flowing in a microfluidic chip at a throughput of 150 grains per second. Electrical signals and synchronized optical images are processed by two independent machine learning-based classifiers, whose predictions are then combined to provide the final classification outcome.The applicability of the method is demonstrated in a proof-of-concept classification experiment involving eight pollen classes from different taxa. The average balanced accuracy is 78.7% for the electrical classifier, 76.7% for the optical classifier and 84.2% for the multimodal classifier. The accuracy is 82.8% for the electrical classifier, 84.1% for the optical classifier and 88.3% for the multimodal classifier.The multimodal approach provides better classification results with respect to the analysis based on electrical or optical features alone.The proposed methodology paves the way for automated multimodal palynology. Moreover, it can be extended to other fields, such as diagnostics and cell therapy, where it could be used for label-free identification of cell populations in heterogeneous samples.
科研通智能强力驱动
Strongly Powered by AbleSci AI