Deep-learning algorithm to detect anomalies in compressed breast: A numerical study

Ganesh M. Balasubramaniam; Shlomi Arnon

Deep-learning algorithm to detect anomalies in compressed breast: A numerical study

Ganesh M. Balasubramaniam, Shlomi Arnon

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Article number	DTu3A.5
Journal	Optics InfoBase Conference Papers
State	Published - 1 Jan 2021
Event	Bio-Optics: Design and Application, BODA 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021 - Virtual, Online, United States Duration: 12 Apr 2021 → 16 Apr 2021

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

Cite this

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title = "Deep-learning algorithm to detect anomalies in compressed breast: A numerical study",

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.",

author = "Balasubramaniam, {Ganesh M.} and Shlomi Arnon",

note = "Publisher Copyright: {\textcopyright} 2021 The Author (s).; Bio-Optics: Design and Application, BODA 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021 ; Conference date: 12-04-2021 Through 16-04-2021",

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T1 - Deep-learning algorithm to detect anomalies in compressed breast

T2 - Bio-Optics: Design and Application, BODA 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

AU - Balasubramaniam, Ganesh M.

AU - Arnon, Shlomi

PY - 2021/1/1

Y1 - 2021/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85119338228&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85119338228

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - DTu3A.5

Y2 - 12 April 2021 through 16 April 2021

ER -

Deep-learning algorithm to detect anomalies in compressed breast: A numerical study

Abstract

ASJC Scopus subject areas

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