Training artificial neural networks using self-organizing migrating algorithm for skin segmentation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256936" target="_blank" >RIV/61989100:27240/24:10256936 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/24:10256936

Result on the web

<a href="https://www.nature.com/articles/s41598-024-72884-0" target="_blank" >https://www.nature.com/articles/s41598-024-72884-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-024-72884-0" target="_blank" >10.1038/s41598-024-72884-0</a>

Result language

angličtina

Original language name

Training artificial neural networks using self-organizing migrating algorithm for skin segmentation

Original language description

This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based optimization methods such as ADAM and SGDM, as well as with another evolutionary algorithm, differential evolution (DE). Experiments are conducted on the skin dataset, which consists of 245,057 samples with skin and non-skin labels. The results show that the neural network trained by SOMA achieves the highest accuracy (93.18%), outperforming ADAM (84.87%), SGDM (84.79%), and DE (91.32%). The visual evaluation also reveals the SOMA-trained neural network’s accurate and reliable segmentation capabilities in most cases. These findings highlight the potential of incorporating evolutionary optimization algorithms like SOMA into the training process of artificial neural networks, significantly improving performance in image segmentation tasks. © The Author(s) 2024.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

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Volume of the periodical

14

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

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UT code for WoS article

001326080400039

EID of the result in the Scopus database

2-s2.0-85205714007