Recurrence quantification as potential bio-markers for diagnosis of pre-cancer

Sabyasachi Mukhopadhyay; Sawon Pratiher; Ritwik Barman; Souvik Pratiher; Asima Pradhan; Nirmalya Ghosh; Prasanta K. Panigrahi

doi:10.1117/12.2251235

3 March 2017 Recurrence quantification as potential bio-markers for diagnosis of pre-cancer

Sabyasachi Mukhopadhyay, Sawon Pratiher, Ritwik Barman, Souvik Pratiher, Asima Pradhan, Nirmalya Ghosh, Prasanta K. Panigrahi

Author Affiliations +

Proceedings Volume 10063, Dynamics and Fluctuations in Biomedical Photonics XIV; 1006310 (2017) https://doi.org/10.1117/12.2251235
Event: SPIE BiOS, 2017, San Francisco, California, United States

Abstract

In this paper, the spectroscopy signals have been analyzed in recurrence plots (RP), and extract recurrence quantification analysis (RQA) parameters from the RP in order to classify the tissues into normal and different precancerous grades. Three RQA parameters have been quantified in order to extract the important features in the spectroscopy data. These features have been fed to different classifiers for classification. Simulation results validate the efficacy of the recurrence quantification as potential bio-markers for diagnosis of pre-cancer.

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Sabyasachi Mukhopadhyay, Sawon Pratiher, Ritwik Barman, Souvik Pratiher, Asima Pradhan, Nirmalya Ghosh, and Prasanta K. Panigrahi "Recurrence quantification as potential bio-markers for diagnosis of pre-cancer", Proc. SPIE 10063, Dynamics and Fluctuations in Biomedical Photonics XIV, 1006310 (3 March 2017); https://doi.org/10.1117/12.2251235

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