Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity

Poulami Samaddar, Anup Kumar Mishra, Sunil Gaddam, Mansunderbir Singh, Vaishnavi K. Modi, Keerthy Gopalakrishnan, Rachel L. Bayer, Ivone Cristina Igreja Sa, Shalil Khanal, Petra Hirsova, Enis Kostallari, Shuvashis Dey, Dipankar Mitra, Sayan Roy, Shivaram P. Arunachalam

Research output: Contribution to journalArticlepeer-review


The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity ((Formula presented.)) and conductivity ((Formula presented.)), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

Original languageEnglish (US)
Article number9919
Issue number24
StatePublished - Dec 2022


  • dielectric spectroscopy
  • fibrosis
  • machine learning
  • microwave
  • non-alcoholic steatohepatitis
  • relative permittivity

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry


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