Deep learning based processing framework for spatio-temporal analysis and classification of laser biospeckle data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50020700" target="_blank" >RIV/62690094:18450/24:50020700 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0030399223010319?pes=vor" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0030399223010319?pes=vor</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.optlastec.2023.110138" target="_blank" >10.1016/j.optlastec.2023.110138</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep learning based processing framework for spatio-temporal analysis and classification of laser biospeckle data

Popis výsledku v původním jazyce

Laser biospeckle is an advanced optical technique with the ability to non-destructively visualize various transient phenomenon via spatial and temporal statistics. However, accuracy of the existing image processing techniques used to process biospeckle data is hampered by various experimental and processing dependent factors. Therefore, in this work, a novel 3D convolution neural network (3D CNN) based deep learning (DL) architecture is developed for spatio-temporal analysis of biospeckle data in both qualitative and quantitative domains that effectively reduces errors introduced due to the influence of varying experimental parameters. Firstly, 3D CNN based image processing model is proposed for spatio-temporal analysis and classification of biospeckle data. Furthermore, a novel DL based numerical indexing strategy is developed for identification of level of activity in a sample. Finally, impact of varying experimental parameters on accuracy of the proposed technique is analyzed. In this direction, multiple experiments were performed to examine the effect of variation in input data parameters such as frame dimension, frame rate, number of frames, and background noise on accuracy of the trained model. Performance of the proposed model was analyzed and compared with respect to synthetic data generated by using rotating diffuser based simulation model. Robustness of the proposed strategy was also validated experimentally on practical data associated with identification of disease in seeds. Obtained results demonstrated that the proposed technique is accurate and can perform spatio-temporal classification and numerical indexing of the biospeckle data under varying experimental parameters. © 2023 Elsevier Ltd

Název v anglickém jazyce

Deep learning based processing framework for spatio-temporal analysis and classification of laser biospeckle data

Popis výsledku anglicky

Laser biospeckle is an advanced optical technique with the ability to non-destructively visualize various transient phenomenon via spatial and temporal statistics. However, accuracy of the existing image processing techniques used to process biospeckle data is hampered by various experimental and processing dependent factors. Therefore, in this work, a novel 3D convolution neural network (3D CNN) based deep learning (DL) architecture is developed for spatio-temporal analysis of biospeckle data in both qualitative and quantitative domains that effectively reduces errors introduced due to the influence of varying experimental parameters. Firstly, 3D CNN based image processing model is proposed for spatio-temporal analysis and classification of biospeckle data. Furthermore, a novel DL based numerical indexing strategy is developed for identification of level of activity in a sample. Finally, impact of varying experimental parameters on accuracy of the proposed technique is analyzed. In this direction, multiple experiments were performed to examine the effect of variation in input data parameters such as frame dimension, frame rate, number of frames, and background noise on accuracy of the trained model. Performance of the proposed model was analyzed and compared with respect to synthetic data generated by using rotating diffuser based simulation model. Robustness of the proposed strategy was also validated experimentally on practical data associated with identification of disease in seeds. Obtained results demonstrated that the proposed technique is accurate and can perform spatio-temporal classification and numerical indexing of the biospeckle data under varying experimental parameters. © 2023 Elsevier Ltd

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF18_069%2F0010054" target="_blank" >EF18_069/0010054: IT4Neuro(degeneration)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Optics and Laser Technology

ISSN

0030-3992

e-ISSN

1879-2545

Svazek periodika

169

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

"Article number: 110138"

Kód UT WoS článku

001158706500001

EID výsledku v databázi Scopus

2-s2.0-85172891539