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Integrating optical and electrical sensing with machine learning for advanced particle characterization.
Kokabi, Mahtab; Tayyab, Muhammad; Rather, Gulam M; Pournadali Khamseh, Arastou; Cheng, Daniel; DeMauro, Edward P; Javanmard, Mehdi.
Afiliación
  • Kokabi M; Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
  • Tayyab M; Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
  • Rather GM; Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, 08901, USA.
  • Pournadali Khamseh A; Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
  • Cheng D; Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
  • DeMauro EP; Department of Mechanical and Aerospace Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
  • Javanmard M; Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA. mehdi.javanmard@rutgers.edu.
Biomed Microdevices ; 26(2): 25, 2024 May 23.
Article en En | MEDLINE | ID: mdl-38780704
ABSTRACT
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.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Aprendizaje Automático Idioma: En Revista: Biomed Microdevices Asunto de la revista: ENGENHARIA BIOMEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Aprendizaje Automático Idioma: En Revista: Biomed Microdevices Asunto de la revista: ENGENHARIA BIOMEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos