Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
Conferences > LS > 2021 > JW7A > JW7A.10

Detecting Breast Tumor by Photoacoustic Spectroscopy Integrated Machine Learning: A Comparison of Statistical and Algorithm Based Features

Jackson Rodrigues, Ashwini Amin, Subhash Chandra, G Subramanya Nayak, Satadru Ray, K Satyamoorthy, and K K Mahato.

Not Accessible

Your library or personal account may give you access

Abstract

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. Comments and questions should be directed to the OSA Conference Papers staff.

© 2021 The Author(s)

PDF Article  |   Presentation Video
More Like This
Discriminatory potential of photoacoustic spectroscopic fingerprints integrated with machine learning to distinguish between different organs: ex vivo

Jackson Rodrigues, Akhil K A, and Krishna Kishore Mahato
FTh3B.5 Frontiers in Optics (FiO) 2022

Classifying Breast Cancer Cell Lines of Different Metastasis Potentials using Visible Resonance Raman Spectroscopy and Machine Learning

Binlin Wu, Lin Zhang, Kenneth Jimenez, Susie Boydston-White, Eric Wang, Cheng-hui Liu, and Robert R Alfano
AW2T.5 CLEO: Applications and Technology (CLEO:A&T) 2021

Deep-Learning Algorithm To Detect Anomalies In Compressed Breast: A Numerical Study

Ganesh M. Balasubramaniam and Shlomi Arnon
DTu3A.5 Bio-Optics: Design and Application (BODA) 2021

View More...

Poster Presentation

Media 1: PDF (481 KB)     

Presentation Video

Presentation video access is available to:

  1. Optica Publishing Group subscribers
  2. Technical meeting attendees
  3. Optica members who wish to use one of their free downloads. Please download the article first. After downloading, please refresh this page.

Contact your librarian or system administrator
or
Log in to access Optica Member Subscription or free downloads

More Like This
Discriminatory potential of photoacoustic spectroscopic fingerprints integrated with machine learning to distinguish between different organs: ex vivo

Jackson Rodrigues, Akhil K A, and Krishna Kishore Mahato
FTh3B.5 Frontiers in Optics (FiO) 2022

Classifying Breast Cancer Cell Lines of Different Metastasis Potentials using Visible Resonance Raman Spectroscopy and Machine Learning

Binlin Wu, Lin Zhang, Kenneth Jimenez, Susie Boydston-White, Eric Wang, Cheng-hui Liu, and Robert R Alfano
AW2T.5 CLEO: Applications and Technology (CLEO:A&T) 2021

Deep-Learning Algorithm To Detect Anomalies In Compressed Breast: A Numerical Study

Ganesh M. Balasubramaniam and Shlomi Arnon
DTu3A.5 Bio-Optics: Design and Application (BODA) 2021

View More...
Benefit of extending near-infrared wavelength range of diffuse reflectance spectroscopy for colorectal cancer detection using machine learning

Marcelo Saito Nogueira, Siddra Maryam, Michael Amissah, Noel Lynch, Shane Killeen, Micheal O’Riordain, and Stefan Andersson-Engels
EW4A.16 European Conference on Biomedical Optics (ECBO) 2021

Evaluating Biomarker Features for Lung Cancer Using Machine Learning

P. Viswanathan, S. Prabhala, J. Lin, H.K. Roy, H. Subramanian, and V. Backman
DF1A.5 Bio-Optics: Design and Application (BODA) 2021

Select as filters
Select Topics Cancel
Top
       
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
Privacy | Terms of Use