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Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model

Recently, artificial intelligence (AI) with deep learning (DL) and machine learning (ML) has been extensively used to automate labor-intensive and time-consuming work and to help in prognosis and diagnosis. AI’s role in biomedical and biological imaging is an emerging field of research and reveals f...

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Autores principales: A. Mansouri, Rasha, Ragab, Mahmoud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819283/
https://www.ncbi.nlm.nih.gov/pubmed/36611515
http://dx.doi.org/10.3390/healthcare11010055
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author A. Mansouri, Rasha
Ragab, Mahmoud
author_facet A. Mansouri, Rasha
Ragab, Mahmoud
author_sort A. Mansouri, Rasha
collection PubMed
description Recently, artificial intelligence (AI) with deep learning (DL) and machine learning (ML) has been extensively used to automate labor-intensive and time-consuming work and to help in prognosis and diagnosis. AI’s role in biomedical and biological imaging is an emerging field of research and reveals future trends. Cervical cell (CCL) classification is crucial in screening cervical cancer (CC) at an earlier stage. Unlike the traditional classification method, which depends on hand-engineered or crafted features, convolution neural network (CNN) usually categorizes CCLs through learned features. Moreover, the latent correlation of images might be disregarded in CNN feature learning and thereby influence the representative capability of the CNN feature. This study develops an equilibrium optimizer with ensemble learning-based cervical precancerous lesion classification on colposcopy images (EOEL-PCLCCI) technique. The presented EOEL-PCLCCI technique mainly focuses on identifying and classifying cervical cancer on colposcopy images. In the presented EOEL-PCLCCI technique, the DenseNet-264 architecture is used for the feature extractor, and the EO algorithm is applied as a hyperparameter optimizer. An ensemble of weighted voting classifications, namely long short-term memory (LSTM) and gated recurrent unit (GRU), is used for the classification process. A widespread simulation analysis is performed on a benchmark dataset to depict the superior performance of the EOEL-PCLCCI approach, and the results demonstrated the betterment of the EOEL-PCLCCI algorithm over other DL models.
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spelling pubmed-98192832023-01-07 Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model A. Mansouri, Rasha Ragab, Mahmoud Healthcare (Basel) Article Recently, artificial intelligence (AI) with deep learning (DL) and machine learning (ML) has been extensively used to automate labor-intensive and time-consuming work and to help in prognosis and diagnosis. AI’s role in biomedical and biological imaging is an emerging field of research and reveals future trends. Cervical cell (CCL) classification is crucial in screening cervical cancer (CC) at an earlier stage. Unlike the traditional classification method, which depends on hand-engineered or crafted features, convolution neural network (CNN) usually categorizes CCLs through learned features. Moreover, the latent correlation of images might be disregarded in CNN feature learning and thereby influence the representative capability of the CNN feature. This study develops an equilibrium optimizer with ensemble learning-based cervical precancerous lesion classification on colposcopy images (EOEL-PCLCCI) technique. The presented EOEL-PCLCCI technique mainly focuses on identifying and classifying cervical cancer on colposcopy images. In the presented EOEL-PCLCCI technique, the DenseNet-264 architecture is used for the feature extractor, and the EO algorithm is applied as a hyperparameter optimizer. An ensemble of weighted voting classifications, namely long short-term memory (LSTM) and gated recurrent unit (GRU), is used for the classification process. A widespread simulation analysis is performed on a benchmark dataset to depict the superior performance of the EOEL-PCLCCI approach, and the results demonstrated the betterment of the EOEL-PCLCCI algorithm over other DL models. MDPI 2022-12-25 /pmc/articles/PMC9819283/ /pubmed/36611515 http://dx.doi.org/10.3390/healthcare11010055 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
A. Mansouri, Rasha
Ragab, Mahmoud
Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model
title Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model
title_full Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model
title_fullStr Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model
title_full_unstemmed Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model
title_short Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model
title_sort equilibrium optimization algorithm with ensemble learning based cervical precancerous lesion classification model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819283/
https://www.ncbi.nlm.nih.gov/pubmed/36611515
http://dx.doi.org/10.3390/healthcare11010055
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