Autoregressive parametric modeling combined ANOVA approach for label-free-based cancerous and normal cells discrimination

Aysha F. AbdulGani, Mahmoud Al Ahmad

Research output: Contribution to journalArticlepeer-review

Abstract

Label free based methods received huge interest in the field of bio cell characterizations because they do not cause any cell damage nor contribute any change in its compositions. This work takes a close outlook of cancerous cells discrimination from normal cells utilizing parametric modeling approach. Autoregressive (AR) modeling technique is used to fit the measured optical transmittance profiles of both cancer and normal cells. The transmitted light intensity, when passes through the cells, gets affected by their intercellular compositions and membrane properties. In this study, four types of cells: lung-cancerous and normal, liver-cancerous and normal, were suspended in their corresponding medium and their transmission characteristics were collected and processed. The AR coefficients of each type of the cell were analyzed with the statistical technique called Analysis of variance (ANOVA), which provided the significant coefficients. The poles extracted from the significant coefficients resulted in an improved demarcation for normal and cancer cells. These outcomes can be further utilized for cell classification using statistical tools.

Original languageEnglish
Article numbere07027
JournalHeliyon
Volume7
Issue number5
DOIs
Publication statusPublished - May 2021

Keywords

  • ANOVA
  • Autoregressive
  • Cancer
  • Cells
  • Detection
  • Discrimination
  • Poles

ASJC Scopus subject areas

  • General

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