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

Ganesh M. Balasubramaniam; Shlomi Arnon

doi:10.1364/BODA.2021.DTu3A.5

Biophotonics Congress 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper DTu3A.5
•https://doi.org/10.1364/BODA.2021.DTu3A.5

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

Ganesh M. Balasubramaniam and Shlomi Arnon

Bio-Optics: Design and Application 2021

Washington, DC United States
12–16 April 2021
ISBN: 978-1-943580-85-9

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Computational Optics (DTu3A)

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G. M. Balasubramaniam and S. Arnon, "Deep-Learning Algorithm To Detect Anomalies In Compressed Breast: A Numerical Study," in Biophotonics Congress 2021, C. Boudoux, K. Maitland, C. Hendon, M. Wojtkowski, K. Quinn, M. Schanne-Klein, N. Durr, D. Elson, F. Cichos, L. Oddershede, V. Emiliani, O. Maragò, S. Nic Chormaic, N. Pégard, S. Gibbs, S. Vinogradov, M. Niedre, K. Samkoe, A. Devor, D. Peterka, P. Blinder, and E. Buckley, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper DTu3A.5.

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Abstract

A deep-learning algorithm is employed to detect simulated anomalies inside compressed breasts using near-infrared light. Anomaly detection is improved by 55% after employing the algorithm according to the Dice similarity coefficient.

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