Integrating optical and electrical sensing with machine learning for advanced particle characterization.
Particle classification plays a crucial role in various scientific and technological applications, such as differentiating between bacteria and viruses in healthcare applications or identifying and classifying cancer cells. This technique requires accurate and efficient analysis of particle properti...
| Publicado en: | Biomedical Microdevices Vol. 26; no. 2; pp. 1 - 16 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177447879&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177447879 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13872176 ODN jtl: Biomedical Microdevices issn: 13872176 maglogo: N pubinfo: dt: Jun2024 vid: 26 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177447879 10.1007/s10544-024-00707-0 177447879 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Integrating optical and electrical sensing with machine learning for advanced particle characterization. aug: au: Kokabi, Mahtab Tayyab, Muhammad Rather, Gulam M. Pournadali Khamseh, Arastou Cheng, Daniel DeMauro, Edward P. Javanmard, Mehdi affil: Department of Electrical and Computer Engineering, Rutgers University, 08854, Piscataway, NJ, USA sug: ab: Particle classification plays a crucial role in various scientific and technological applications, such as differentiating between bacteria and viruses in healthcare applications or identifying and classifying cancer cells. This technique requires accurate and efficient analysis of particle properties. In this study, we investigated the integration of electrical and optical features through a multimodal approach for particle classification. Machine learning classifier algorithms were applied to evaluate the impact of combining these measurements. Our results demonstrate the superiority of the multimodal approach over analyzing electrical or optical features independently. We achieved an average test accuracy of 94.9% by integrating both modalities, compared to 66.4% for electrical features alone and 90.7% for optical features alone. This highlights the complementary nature of electrical and optical information and its potential for enhancing classification performance. By leveraging electrical sensing and optical imaging techniques, our multimodal approach provides deeper insights into particle properties and offers a more comprehensive understanding of complex biological systems. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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