BNCNN based diffuse optical imaging

Nazish Murad, Min Chun Pan, Ya Fen Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We proposed and implemented a deep learning scheme using convolution neural networks (CNNs) with batch normalization (BNCNN) to construct a sensor-image DOI computation model with the aim of reconstructing tissue optical-property images as well as identifying and localizing breast tumors. A non-iterative learning reconstruction method was developed to recover optical properties, focusing on one-dimensional convolution layers followed by dense layers. Besides simulated data for model training, validation and testing, for the comparison of model performance, measurement data sets were employed to test on the same trained network which results outperform Tikhonov regularization method and other artificial neural networks as well.

Original languageEnglish
Title of host publicationMultimodal Biomedical Imaging XVII
EditorsFred S. Azar, Xavier Intes, Qianqian Fang
PublisherSPIE
ISBN (Electronic)9781510647756
DOIs
StatePublished - 2022
EventMultimodal Biomedical Imaging XVII 2022 - San Francisco, United States
Duration: 22 Jan 202227 Jan 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11952
ISSN (Print)1605-7422

Conference

ConferenceMultimodal Biomedical Imaging XVII 2022
Country/TerritoryUnited States
CitySan Francisco
Period22/01/2227/01/22

Keywords

  • Batch normalization
  • Convolution deep neural networks
  • diffuse optical imaging
  • Sensor to image domain

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