Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals
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| Автор |
Kulyabin, M.
Zhdanov, A. Dolganov, A. Maier, A. |
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| Тематика |
BIOMEDICAL RESEARCH
CLASSIFICATION CNN DEEP LEARNING ELECTRORETINOGRAM ELECTRORETINOGRAPHY ERG SCALOGRAM TRANSFORMER WAVELET DEEP LEARNING PEDIATRICS WAVELET TRANSFORMS BIOMEDICAL RESEARCH CNN DEEP LEARNING ELECTRORETINOGRAMS ELECTRORETINOGRAPHY MOTHER WAVELETS SCALOGRAM TRANSFORMER WAVELET DIAGNOSIS ARTIFICIAL INTELLIGENCE CHILD ELECTRORETINOGRAPHY HUMAN WAVELET ANALYSIS ARTIFICIAL INTELLIGENCE CHILD ELECTRORETINOGRAPHY HUMANS WAVELET ANALYSIS |
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| Описание |
The continuous advancements in healthcare technology have empowered the discovery, diagnosis, and prediction of diseases, revolutionizing the field. Artificial intelligence (AI) is expected to play a pivotal role in achieving the goals of precision medicine, particularly in disease prevention, detection, and personalized treatment. This study aims to determine the optimal combination of the mother wavelet and AI model for the analysis of pediatric electroretinogram (ERG) signals. The dataset, consisting of signals and corresponding diagnoses, undergoes Continuous Wavelet Transform (CWT) using commonly used wavelets to obtain a time-frequency representation. Wavelet images were used for the training of five widely used deep learning models: VGG-11, ResNet-50, DensNet-121, ResNext-50, and Vision Transformer, to evaluate their accuracy in classifying healthy and unhealthy patients. The findings demonstrate that the combination of Ricker Wavelet and Vision Transformer consistently yields the highest median accuracy values for ERG analysis, as evidenced by the upper and lower quartile values. The median balanced accuracy of the obtained combination of the three considered types of ERG signals in the article are 0.83, 0.85, and 0.88. However, other wavelet types also achieved high accuracy levels, indicating the importance of carefully selecting the mother wavelet for accurate classification. The study provides valuable insights into the effectiveness of different combinations of wavelets and models in classifying ERG wavelet scalograms. © 2023 by the authors.
Ministry of Education and Science of the Russian Federation, Minobrnauka The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority—2030 Program) is gratefully acknowledged. |
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| Дата |
2024-04-05T16:28:11Z
2024-04-05T16:28:11Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Kulyabin, M, Zhdanov, A, Dolganov, A & Maier, A 2023, 'Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals', Sensors, Том. 23, № 13, 5813. https://doi.org/10.3390/s23135813
Kulyabin, M., Zhdanov, A., Dolganov, A., & Maier, A. (2023). Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals. Sensors, 23(13), [5813]. https://doi.org/10.3390/s23135813 1424-8220 Final All Open Access, Gold, Green https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164845457&doi=10.3390%2fs23135813&partnerID=40&md5=2b686cd09772d89851c3fac73b9343df https://www.mdpi.com/1424-8220/23/13/5813/pdf?version=1687418907 http://elar.urfu.ru/handle/10995/130641 10.3390/s23135813 85164845457 001030226700001 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
Multidisciplinary Digital Publishing Institute (MDPI)
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| Источник |
Sensors
Sensors |
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