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Deep features using convolutional neural network for early stage cancer detection
Author(s): Sawon Pratiher; Shubhobrata Bhattacharya; Sabyasachi Mukhopadhyay; Nirmalya Ghosh; Gautham Pasupuleti; Prasanta K. Panigrahi
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Paper Abstract

In this contribution, we have done exploratory experiments using deep learning framework to classify elastic scattering spectra of biological tissues into normal and cancerous ones. An analytical assessment highlighting the superiority of convolutional neural network (CNN) extracted deep features over classical hand crafted biomarkers is discussed. The proposed method employs elastic scattering spectra of the tissues as input to CNN and thereby, averting the requirement of domain experts for extraction of diagnostic feature descriptors. Experimental results are discussed in detail.

Paper Details

Date Published: 24 May 2018
PDF: 6 pages
Proc. SPIE 10679, Optics, Photonics, and Digital Technologies for Imaging Applications V, 1067902 (24 May 2018); doi: 10.1117/12.2300024
Show Author Affiliations
Sawon Pratiher, Indian Institute of Technology Kanpur (India)
Shubhobrata Bhattacharya, Indian Institute of Technology Kanpur (India)
Sabyasachi Mukhopadhyay, Indian Institute of Science Education and Research Kolkata (India)
Nirmalya Ghosh, Indian Institute of Science Education and Research Kolkata (India)
Gautham Pasupuleti, Biodesign Innovation Labs (India)
Prasanta K. Panigrahi, Indian Institute of Science Education and Research Kolkata (India)


Published in SPIE Proceedings Vol. 10679:
Optics, Photonics, and Digital Technologies for Imaging Applications V
Peter Schelkens; Touradj Ebrahimi; Gabriel Cristóbal, Editor(s)

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