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 reve...

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Bibliographic Details
Published in:Healthcare (Basel) 2022-12, Vol.11 (1), p.55
Main Authors: A Mansouri, Rasha, Ragab, Mahmoud
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Automation
Cancer
Cancer cells
Cervical cancer
Classification
Computational linguistics
Datasets
Diagnosis
Equilibrium
Exploitation
Forecasts and trends
Health aspects
Human papillomavirus
Identification and classification
Language processing
Machine learning
Mathematical optimization
Natural language interfaces
Neural networks
Pap smear
Risk factors
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Summary: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.
ISSN:2227-9032
2227-9032
DOI:10.3390/healthcare11010055