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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9819283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT amansourirasha equilibriumoptimizationalgorithmwithensemblelearningbasedcervicalprecancerouslesionclassificationmodel AT ragabmahmoud equilibriumoptimizationalgorithmwithensemblelearningbasedcervicalprecancerouslesionclassificationmodel |