TY - GEN
T1 - Detecting Breast Tumor by Photoacoustic Spectroscopy Integrated Machine Learning
T2 - Laser Science, LS 2021 - Part of Frontiers in Optics, FiO 2021
AU - Rodrigues, Jackson
AU - Amin, Ashwini
AU - Chandra, Subhash
AU - Nayak, G. Subramanya
AU - Ray, Satadru
AU - Satyamoorthy, K.
AU - Mahato, K. K.
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 creating necessary infrastructure and facilities at Manipal School of Life Sciences used to conduct this study. Jackson Rodrigues thanks to the 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:
© Optica Publishing Group 2021
PY - 2021
Y1 - 2021
N2 - We compared the performance of feature selection over feature extraction on wavelet packet decomposed photoacoustic spectra belonging to control and different time-points of breast tumor progression ex vivo, in machine learning based classification.
AB - We compared the performance of feature selection over feature extraction on wavelet packet decomposed photoacoustic spectra belonging to control and different time-points of breast tumor progression ex vivo, in machine learning based classification.
UR - http://www.scopus.com/inward/record.url?scp=85130255190&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85130255190
T3 - Optics InfoBase Conference Papers
BT - Laser Science, LS 2021
PB - Optica Publishing Group (formerly OSA)
Y2 - 1 November 2021 through 4 November 2021
ER -