Analysis of the effectiveness of two different loss functions in training a neural network in lung image reconstruction using impedance tomography

The article presents research findings on developing a medical diagnostic system based on electrical impedance tomography technology. One of the key components of this project is developing a method for reconstructing the structure of human lungs using this technology. The authors of the article com...

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Bibliographic Details
Published in:Journal of Modern Science Vol. 57; no. 3; pp. 594 - 608
Main Authors: Lalak-Dybała, Małgorzata, Stefaniak, Barbara, Olszewski, Paweł
Format: Journal Article
Language:English
Published: Wydawnictwo Akademii Nauk Stosowanych WSGE im. A. De Gasperi w Józefowie 2024
WSGE University of Applied Sciences in Józefów Publishing House
Akademia Nauk Stosowanych WSGE im. A. De Gasperi w Józefowie
Subjects:
convolutional neural network
Economy
electrical impedance tomography
lung reconstruction
neural network with encoder-decoder architecture
resnet
Electrical impedance tomography
Lung reconstruction
convolutional neural network
neural network with encoderdecoder architecture
ResNet
ISSN:1734-2031, 2391-789X
Online Access:Get full text
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Summary:The article presents research findings on developing a medical diagnostic system based on electrical impedance tomography technology. One of the key components of this project is developing a method for reconstructing the structure of human lungs using this technology. The authors of the article compared the effectiveness of two different loss functions in training a neural network, which is tasked with accurately replicating the lung structure based on electrical impedance tomography data. The researchers analyzed various approaches to calculating loss functions, including cosine embedding loss and InfoNCE loss. They compared the results obtained using these two functions to identify which one performs better in lung structure reconstruction. The findings of these studies may have significant implications for the development of diagnostic systems based on electrical impedance tomography and for improving the effectiveness of lung disease diagnosis. Additionally, the authors discuss potential future directions for the project, including possible applications of the research findings in clinical practice. Future research efforts may focus on optimizing neural network parameters, exploring alternative loss functions, or utilizing advanced machine learning techniques for even more precise lung structure reconstruction. The pursuit of improving such diagnostic systems could lead to significant advancements in the field of medicine, particularly in diagnosing and treating respiratory diseases.
ISSN:1734-2031
2391-789X
DOI:10.13166/jms/191304