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Automated laryngeal mass detection algorithm for home-based self-screening test based on convolutional neural network

Authors
  • Kim, Gun Ho1
  • Sung, Eui-Suk2, 2
  • Nam, Kyoung Won2, 2, 3
  • 1 Pusan National University, Busan, South Korea , Busan (South Korea)
  • 2 Pusan National University Yangsan Hospital, Yangsan, South Korea , Yangsan (South Korea)
  • 3 Pusan National University, 49 Busandaehak-ro, Mulgeum-eup, Yangsan, Gyeongsangnam-do, 50629, South Korea , Yangsan (South Korea)
Type
Published Article
Journal
BioMedical Engineering OnLine
Publisher
Springer (Biomed Central Ltd.)
Publication Date
May 25, 2021
Volume
20
Issue
1
Identifiers
DOI: 10.1186/s12938-021-00886-4
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundEarly detection of laryngeal masses without periodic visits to hospitals is essential for improving the possibility of full recovery and the long-term survival ratio after prompt treatment, as well as reducing the risk of clinical infection.ResultsWe first propose a convolutional neural network model for automated laryngeal mass detection based on diagnostic images captured at hospitals. Thereafter, we propose a pilot system, composed of an embedded controller, a camera module, and an LCD display, that can be utilized for a home-based self-screening test. In terms of evaluating the model’s performance, the experimental results indicated a final validation loss of 0.9152 and a F1-score of 0.8371 before post-processing. Additionally, the F1-score of the original computer algorithm with respect to 100 randomly selected color-printed test images was 0.8534 after post-processing while that of the embedded pilot system was 0.7672.ConclusionsThe proposed technique is expected to increase the ratio of early detection of laryngeal masses without the risk of clinical infection spread, which could help improve convenience and ensure safety of individuals, patients, and medical staff.

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