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http://elar.urfu.ru/handle/10995/130641
Название: | Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals |
Авторы: | Kulyabin, M. Zhdanov, A. Dolganov, A. Maier, A. |
Дата публикации: | 2023 |
Издатель: | Multidisciplinary Digital Publishing Institute (MDPI) |
Библиографическое описание: | 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 |
Аннотация: | 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. |
Ключевые слова: | 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 |
URI: | http://elar.urfu.ru/handle/10995/130641 |
Условия доступа: | info:eu-repo/semantics/openAccess cc-by |
Текст лицензии: | https://creativecommons.org/licenses/by/4.0/ |
Идентификатор SCOPUS: | 85164845457 |
Идентификатор WOS: | 001030226700001 |
Идентификатор PURE: | 41993506 |
ISSN: | 1424-8220 |
DOI: | 10.3390/s23135813 |
Сведения о поддержке: | 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. |
Располагается в коллекциях: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
Файлы этого ресурса:
Файл | Описание | Размер | Формат | |
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2-s2.0-85164845457.pdf | 1,62 MB | Adobe PDF | Просмотреть/Открыть |
Лицензия на ресурс: Лицензия Creative Commons