TY - GEN
T1 - Discriminatory potential of photoacoustic spectroscopic fingerprints integrated with machine learning to distinguish between different organs
T2 - Frontiers in Optics, FiO 2022
AU - Rodrigues, Jackson
AU - Akhil, K. A.
AU - Mahato, Krishna Kishore
N1 - Funding Information:
The authors thank DBT, Government of India (Sanction ID: BTPR/14776/MED/32/460/2015), for financial support, and Manipal Academy of Higher Education, Manipal, India, for providing necessary infrastructure and facilities at Manipal School of Life Sciences. Jackson Rodrigues thanks ICMR, Government of India, New Delhi, for granting SRF to him (FileNo-5/3/8/45/ITR-F/2019-ITR-IRIS Cell No. 2019-5005).
Publisher Copyright:
© 2022 The Author (s)
PY - 2022
Y1 - 2022
N2 - Photoacoustic signatures from different organs like heart, kidney, liver, lungs, and spleen were recorded and subjected to machine-learning-based analysis for discrimination. The outcomes clearly suggest potentiality of machine-learning-enabled photoacoustic spectroscopy in organs classification.
AB - Photoacoustic signatures from different organs like heart, kidney, liver, lungs, and spleen were recorded and subjected to machine-learning-based analysis for discrimination. The outcomes clearly suggest potentiality of machine-learning-enabled photoacoustic spectroscopy in organs classification.
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M3 - Conference contribution
AN - SCOPUS:85146750890
T3 - Optics InfoBase Conference Papers
BT - Frontiers in Optics, FiO 2022
PB - Optica Publishing Group (formerly OSA)
Y2 - 17 October 2022 through 20 October 2022
ER -